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1506.01186
| 31 |
The ï¬rst step is to estimate the stepsize setting. Since the architecture uses a batchsize of 128 an epoch is equal to 1, 281, 167/128 = 10, 009 iterations. Hence, good settings for stepsize would be 20, 000, 30, 000, or possibly 40, 000. The results in this section are based on stepsize = 30000. The next step is to estimate the bounds for the learning rate, which is found with the LR range test by making a run for 4 epochs where the learning rate linearly increases from 0.001 to 0.065 (Figure 11). This ï¬gure shows that one can use bounds between 0.01 and 0.04 and still have the model reach convergence. However, learning rates above 0.025 cause the training to converge erratically. For both triangular2 and the exp range policies, the base lr was set to 0.01 and max lr was set to 0.026. As above, the accuracy peaks for both these learning rate policies corre- spond to the same learning rate value as the f ixed and exp policies. Hence, the comparisons below will focus on the peak accuracies from the LCR methods.
|
1506.01186#31
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
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] |
{
"authors": "Leslie N. Smith",
"chunk_id": 31,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "The ï¬rst step is to estimate the stepsize setting. Since the architecture uses a batchsize of 128 an epoch is equal to 1, 281, 167/128 = 10, 009 iterations. Hence, good settings for stepsize would be 20, 000, 30, 000, or possibly 40, 000. The results in this section are based on stepsize = 30000. The next step is to estimate the bounds for the learning rate, which is found with the LR range test by making a run for 4 epochs where the learning rate linearly increases from 0.001 to 0.065 (Figure 11). This ï¬gure shows that one can use bounds between 0.01 and 0.04 and still have the model reach convergence. However, learning rates above 0.025 cause the training to converge erratically. For both triangular2 and the exp range policies, the base lr was set to 0.01 and max lr was set to 0.026. As above, the accuracy peaks for both these learning rate policies corre- spond to the same learning rate value as the f ixed and exp policies. Hence, the comparisons below will focus on the peak accuracies from the LCR methods.",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 32 |
Figure 12 compares the results of running with the f ixed versus the triangular2 policy for this architecture (due to time limitations, each training stage was not run until it fully plateaued). In this case, the peaks at the end of each cycle for the triangular2 policy produce better accuracies than the f ixed policy. The ï¬nal accuracy shows an improvement from the network trained by the triangular2 policy (Ta- ble 1) to be 1.4% better than the accuracy from the f ixed policy. This demonstrates that the triangular2 policy im- proves on a âbest guessâ for a ï¬xed learning rate.
Figure 13 compares the results of running with the exp versus the exp range policy with gamma = 0.99998. Once again, the peaks at the end of each cycle for the
Imagenet with GoogLeNet architecture Ld © = Validation Accuracy ° hed ie -â ExpLR âExp range 0 0.5 1 15 2 Iteration x10
Figure 13. Validation data classiï¬cation accuracy as a function of iteration for exp versus exp range.
exp range policy produce better validation accuracies than the exp policy. The ï¬nal accuracy from the exp range pol- icy (Table 1) is 2% better than from the exp policy.
# 5. Conclusions
|
1506.01186#32
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 32,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "Figure 12 compares the results of running with the f ixed versus the triangular2 policy for this architecture (due to time limitations, each training stage was not run until it fully plateaued). In this case, the peaks at the end of each cycle for the triangular2 policy produce better accuracies than the f ixed policy. The ï¬nal accuracy shows an improvement from the network trained by the triangular2 policy (Ta- ble 1) to be 1.4% better than the accuracy from the f ixed policy. This demonstrates that the triangular2 policy im- proves on a âbest guessâ for a ï¬xed learning rate.\nFigure 13 compares the results of running with the exp versus the exp range policy with gamma = 0.99998. Once again, the peaks at the end of each cycle for the\nImagenet with GoogLeNet architecture Ld © = Validation Accuracy ° hed ie -â ExpLR âExp range 0 0.5 1 15 2 Iteration x10\nFigure 13. Validation data classiï¬cation accuracy as a function of iteration for exp versus exp range.\nexp range policy produce better validation accuracies than the exp policy. The ï¬nal accuracy from the exp range pol- icy (Table 1) is 2% better than from the exp policy.\n# 5. Conclusions",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 33 |
# 5. Conclusions
The results presented in this paper demonstrate the ben- eï¬ts of the cyclic learning rate (CLR) methods. A short run of only a few epochs where the learning rate linearly in- creases is sufï¬cient to estimate boundary learning rates for the CLR policies. Then a policy where the learning rate cyclically varies between these bounds is sufï¬cient to ob- tain near optimal classiï¬cation results, often with fewer it- erations. This policy is easy to implement and unlike adap- tive learning rate methods, incurs essentially no additional computational expense.
This paper shows that use of cyclic functions as a learn- ing rate policy provides substantial improvements in perfor- mance for a range of architectures. In addition, the cyclic nature of these methods provides guidance as to times to drop the learning rate values (after 3 - 5 cycles) and when to stop the the training. All of these factors reduce the guess- work in setting the learning rates and make these methods practical tools for everyone who trains neural networks.
This work has not explored the full range of applications for cyclic learning rate methods. We plan to determine if equivalent policies work for training different architectures, such as recurrent neural networks. Furthermore, we believe that a theoretical analysis would provide an improved un- derstanding of these methods, which might lead to improve- ments in the algorithms.
|
1506.01186#33
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
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{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 33,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "# 5. Conclusions\nThe results presented in this paper demonstrate the ben- eï¬ts of the cyclic learning rate (CLR) methods. A short run of only a few epochs where the learning rate linearly in- creases is sufï¬cient to estimate boundary learning rates for the CLR policies. Then a policy where the learning rate cyclically varies between these bounds is sufï¬cient to ob- tain near optimal classiï¬cation results, often with fewer it- erations. This policy is easy to implement and unlike adap- tive learning rate methods, incurs essentially no additional computational expense.\nThis paper shows that use of cyclic functions as a learn- ing rate policy provides substantial improvements in perfor- mance for a range of architectures. In addition, the cyclic nature of these methods provides guidance as to times to drop the learning rate values (after 3 - 5 cycles) and when to stop the the training. All of these factors reduce the guess- work in setting the learning rates and make these methods practical tools for everyone who trains neural networks.\nThis work has not explored the full range of applications for cyclic learning rate methods. We plan to determine if equivalent policies work for training different architectures, such as recurrent neural networks. Furthermore, we believe that a theoretical analysis would provide an improved un- derstanding of these methods, which might lead to improve- ments in the algorithms.",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 34 |
# References
[1] K. Bache, D. DeCoste, and P. Smyth. Hot swapping for online adaptation of optimization hyperparameters. arXiv preprint arXiv:1412.6599, 2014. 2
[2] Y. Bengio. Neural Networks: Tricks of the Trade, chap- ter Practical recommendations for gradient-based training of
deep architectures, pages 437â478. Springer Berlin Heidel- berg, 2012. 1, 2, 4
[3] T. M. Breuel. The effects of hyperparameters on sgd training of neural networks. arXiv preprint arXiv:1508.02788, 2015. 2
[4] Y. N. Dauphin, H. de Vries, J. Chung, and Y. Bengio. Rm- sprop and equilibrated adaptive learning rates for non-convex optimization. arXiv preprint arXiv:1502.04390, 2015. 2 [5] J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradi- ent methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121â2159, 2011. 2, 5
|
1506.01186#34
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 34,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "# References\n[1] K. Bache, D. DeCoste, and P. Smyth. Hot swapping for online adaptation of optimization hyperparameters. arXiv preprint arXiv:1412.6599, 2014. 2\n[2] Y. Bengio. Neural Networks: Tricks of the Trade, chap- ter Practical recommendations for gradient-based training of\ndeep architectures, pages 437â478. Springer Berlin Heidel- berg, 2012. 1, 2, 4\n[3] T. M. Breuel. The effects of hyperparameters on sgd training of neural networks. arXiv preprint arXiv:1508.02788, 2015. 2\n[4] Y. N. Dauphin, H. de Vries, J. Chung, and Y. Bengio. Rm- sprop and equilibrated adaptive learning rates for non-convex optimization. arXiv preprint arXiv:1502.04390, 2015. 2 [5] J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradi- ent methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121â2159, 2011. 2, 5",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 35 |
[6] A. P. George and W. B. Powell. Adaptive stepsizes for re- cursive estimation with applications in approximate dynamic programming. Machine learning, 65(1):167â198, 2006. 2 [7] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich fea- ture hierarchies for accurate object detection and semantic segmentation. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 580â587. IEEE, 2014. 1
[8] A. Graves and N. Jaitly. Towards end-to-end speech recog- nition with recurrent neural networks. In Proceedings of the 31st International Conference on Machine Learning (ICML- 14), pages 1764â1772, 2014. 1
[9] C. Gulcehre and Y. Bengio. Adasecant: Robust adap- tive secant method for stochastic gradient. arXiv preprint arXiv:1412.7419, 2014. 2
|
1506.01186#35
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 35,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "[6] A. P. George and W. B. Powell. Adaptive stepsizes for re- cursive estimation with applications in approximate dynamic programming. Machine learning, 65(1):167â198, 2006. 2 [7] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich fea- ture hierarchies for accurate object detection and semantic segmentation. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 580â587. IEEE, 2014. 1\n[8] A. Graves and N. Jaitly. Towards end-to-end speech recog- nition with recurrent neural networks. In Proceedings of the 31st International Conference on Machine Learning (ICML- 14), pages 1764â1772, 2014. 1\n[9] C. Gulcehre and Y. Bengio. Adasecant: Robust adap- tive secant method for stochastic gradient. arXiv preprint arXiv:1412.7419, 2014. 2",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 36 |
[10] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. Computer Vision and Pattern Recog- nition (CVPR), 2016 IEEE Conference on, 2015. 5, 6 [11] K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. arXiv preprint arXiv:1603.05027, 2016. 5, 6
[12] G. Huang, Z. Liu, and K. Q. Weinberger. Densely connected convolutional networks. arXiv preprint arXiv:1608.06993, 2016. 5, 6
[13] G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger. arXiv preprint
Deep networks with stochastic depth. arXiv:1603.09382, 2016. 5, 6 [14] B. Huval, T. Wang, S. Tandon,
|
1506.01186#36
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 36,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "[10] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. Computer Vision and Pattern Recog- nition (CVPR), 2016 IEEE Conference on, 2015. 5, 6 [11] K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. arXiv preprint arXiv:1603.05027, 2016. 5, 6\n[12] G. Huang, Z. Liu, and K. Q. Weinberger. Densely connected convolutional networks. arXiv preprint arXiv:1608.06993, 2016. 5, 6\n[13] G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger. arXiv preprint\nDeep networks with stochastic depth. arXiv:1603.09382, 2016. 5, 6 [14] B. Huval, T. Wang, S. Tandon,",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 37 |
Deep networks with stochastic depth. arXiv:1603.09382, 2016. 5, 6 [14] B. Huval, T. Wang, S. Tandon,
J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, R. Cheng-Yue, F. Mujica, A. Coates, et al. An empirical evaluation of deep learning on highway driving. arXiv preprint arXiv:1504.01716, 2015. 1 [15] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 5
[16] D. Kingma and J. Lei-Ba. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2015. 2, 5
Imagenet classiï¬cation with deep convolutional neural networks. Ad- vances in neural information processing systems, 2012. 1, 2, 6
[18] I. Loshchilov and F. Hutter. Sgdr: Stochastic gradient de- scent with restarts. arXiv preprint arXiv:1608.03983, 2016. 2
|
1506.01186#37
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 37,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "Deep networks with stochastic depth. arXiv:1603.09382, 2016. 5, 6 [14] B. Huval, T. Wang, S. Tandon,\nJ. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, R. Cheng-Yue, F. Mujica, A. Coates, et al. An empirical evaluation of deep learning on highway driving. arXiv preprint arXiv:1504.01716, 2015. 1 [15] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 5\n[16] D. Kingma and J. Lei-Ba. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2015. 2, 5\nImagenet classiï¬cation with deep convolutional neural networks. Ad- vances in neural information processing systems, 2012. 1, 2, 6\n[18] I. Loshchilov and F. Hutter. Sgdr: Stochastic gradient de- scent with restarts. arXiv preprint arXiv:1608.03983, 2016. 2",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 38 |
[19] Y. Nesterov. A method of solving a convex programming In Soviet Mathe- problem with convergence rate o (1/k2). matics Doklady, volume 27, pages 372â376, 1983. 5
[20] S. Ruder. An overview of gradient descent optimization al- gorithms. arXiv preprint arXiv:1600.04747, 2016. 2 [21] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 2015. 6
[22] T. Schaul, S. Zhang, and Y. LeCun. No more pesky learning rates. arXiv preprint arXiv:1206.1106, 2012. 2
[23] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 1
|
1506.01186#38
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 38,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "[19] Y. Nesterov. A method of solving a convex programming In Soviet Mathe- problem with convergence rate o (1/k2). matics Doklady, volume 27, pages 372â376, 1983. 5\n[20] S. Ruder. An overview of gradient descent optimization al- gorithms. arXiv preprint arXiv:1600.04747, 2016. 2 [21] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 2015. 6\n[22] T. Schaul, S. Zhang, and Y. LeCun. No more pesky learning rates. arXiv preprint arXiv:1206.1106, 2012. 2\n[23] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 1",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 39 |
[24] I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In Advances in Neural Infor- mation Processing Systems, pages 3104â3112, 2014. 1 [25] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabi- novich. Going deeper with convolutions. arXiv preprint arXiv:1409.4842, 2014. 1, 2, 7
[26] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face veriï¬ca- tion. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 1701â1708. IEEE, 2014. 1 [27] T. Tieleman and G. Hinton. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4, 2012. 2, 5
|
1506.01186#39
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 39,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "[24] I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In Advances in Neural Infor- mation Processing Systems, pages 3104â3112, 2014. 1 [25] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabi- novich. Going deeper with convolutions. arXiv preprint arXiv:1409.4842, 2014. 1, 2, 7\n[26] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face veriï¬ca- tion. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 1701â1708. IEEE, 2014. 1 [27] T. Tieleman and G. Hinton. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4, 2012. 2, 5",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 41 |
} e l s e i n t i f ( i t r > 0 ) { ( l r p o l i c y == â t r i a n g u l a r â ) { i t r = t h i s â> i t e r â t h i s â>p a r a m . s t a r t i f l r p o l i c y ( ) ; i n t / f l o a t x = ( f l o a t ) x = x / r a t e = t h i s â>p a r a m . b a s e l r ( ) + ( t h i s â>p a r a m . m a x l r () â t h i s â>p a r a m . b a s e l r ( ) ) c y c l e = i t r ( 2 â t h i s â>p a r a m . s t e p s i z e ( ) ) ; ( i t r â ( 2 â c y c l e +1)â t h i s â>p a r a m . s t e p s i z e ( ) ) ; t h i s â>p a r a m . s t e p s i z e ( ) ; â s t d : : max ( d o u b l e ( 0 )
|
1506.01186#41
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 41,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "} e l s e i n t i f ( i t r > 0 ) { ( l r p o l i c y == â t r i a n g u l a r â ) { i t r = t h i s â> i t e r â t h i s â>p a r a m . s t a r t i f l r p o l i c y ( ) ; i n t / f l o a t x = ( f l o a t ) x = x / r a t e = t h i s â>p a r a m . b a s e l r ( ) + ( t h i s â>p a r a m . m a x l r () â t h i s â>p a r a m . b a s e l r ( ) ) c y c l e = i t r ( 2 â t h i s â>p a r a m . s t e p s i z e ( ) ) ; ( i t r â ( 2 â c y c l e +1)â t h i s â>p a r a m . s t e p s i z e ( ) ) ; t h i s â>p a r a m . s t e p s i z e ( ) ; â s t d : : max ( d o u b l e ( 0 )",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 42 |
h i s â>p a r a m . s t e p s i z e ( ) ; â s t d : : max ( d o u b l e ( 0 ) , ( 1 . 0 â f a b s ( x ) ) ) ; } e l s e { r a t e = t h i s â>p a r a m . b a s e l r ( ) ; } } e l s e i n t i f ( i t r > 0 ) { ( l r p o l i c y == â t r i a n g u l a r 2 â ) { i t r = t h i s â> i t e r â t h i s â>p a r a m . s t a r t i f l r p o l i c y ( ) ; i n t / f l o a t x = ( f l o a t ) x = x / r a t e = t h i s â>p a r a m . b a s e l r ( ) + ( t h i s â>p a r a m . m a x l r () â t h i s â>p a r a m . b a s e l r ( ) ) ( 2 â t h i s â>p a r a m . s t e p s i z e ( ) ) ; ( i t
|
1506.01186#42
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 42,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "h i s â>p a r a m . s t e p s i z e ( ) ; â s t d : : max ( d o u b l e ( 0 ) , ( 1 . 0 â f a b s ( x ) ) ) ; } e l s e { r a t e = t h i s â>p a r a m . b a s e l r ( ) ; } } e l s e i n t i f ( i t r > 0 ) { ( l r p o l i c y == â t r i a n g u l a r 2 â ) { i t r = t h i s â> i t e r â t h i s â>p a r a m . s t a r t i f l r p o l i c y ( ) ; i n t / f l o a t x = ( f l o a t ) x = x / r a t e = t h i s â>p a r a m . b a s e l r ( ) + ( t h i s â>p a r a m . m a x l r () â t h i s â>p a r a m . b a s e l r ( ) ) ( 2 â t h i s â>p a r a m . s t e p s i z e ( ) ) ; ( i t",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1506.01186
| 43 |
b a s e l r ( ) ) ( 2 â t h i s â>p a r a m . s t e p s i z e ( ) ) ; ( i t r â ( 2 â c y c l e +1)â t h i s â>p a r a m . s t e p s i z e ( ) ) ; c y c l e = i t r t h i s â>p a r a m . s t e p s i z e ( ) ; â s t d : : min ( d o u b l e ( 1 ) , f a b s ( x ) ) / pow ( 2 . 0 , d o u b l e ( c y c l e ) ) ) ) ; s t d : : max ( d o u b l e ( 0 ) , ( 1 . 0 â } e l s e { r a t e = t h i s â>p a r a m . b a s e l r ( ) ;
|
1506.01186#43
|
Cyclical Learning Rates for Training Neural Networks
|
It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
|
http://arxiv.org/pdf/1506.01186
|
Leslie N. Smith
|
cs.CV, cs.LG, cs.NE
|
Presented at WACV 2017; see https://github.com/bckenstler/CLR for
instructions to implement CLR in Keras
| null |
cs.CV
|
20150603
|
20170404
|
[
{
"id": "1504.01716"
},
{
"id": "1502.03167"
},
{
"id": "1600.04747"
},
{
"id": "1603.05027"
},
{
"id": "1508.02788"
},
{
"id": "1502.04390"
},
{
"id": "1608.03983"
},
{
"id": "1603.09382"
},
{
"id": "1608.06993"
}
] |
{
"authors": "Leslie N. Smith",
"chunk_id": 43,
"doc_id": "1506.01186",
"primary_category": "cs.CV",
"published": 20150603,
"source": "http://arxiv.org/pdf/1506.01186",
"summary": "It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.",
"text": "b a s e l r ( ) ) ( 2 â t h i s â>p a r a m . s t e p s i z e ( ) ) ; ( i t r â ( 2 â c y c l e +1)â t h i s â>p a r a m . s t e p s i z e ( ) ) ; c y c l e = i t r t h i s â>p a r a m . s t e p s i z e ( ) ; â s t d : : min ( d o u b l e ( 1 ) , f a b s ( x ) ) / pow ( 2 . 0 , d o u b l e ( c y c l e ) ) ) ) ; s t d : : max ( d o u b l e ( 0 ) , ( 1 . 0 â } e l s e { r a t e = t h i s â>p a r a m . b a s e l r ( ) ;",
"title": "Cyclical Learning Rates for Training Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 0 |
5 1 0 2
r p A 4 1 ] V C . s c [ 1 v 0 1 4 3 0 . 4 0 5 1 : v i X r a
# Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
Hanjiang Laiâ , Yan Pan ââ¡, Ye Liu§ , and Shuicheng Yanâ
â Department of Electronic and Computer Engineering, National University of Singapore, Singapore â¡School of Software, Sun Yan-Sen University, China § School of Information Science and Technology, Sun Yan-Sen University, China
# Abstract
|
1504.03410#0
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 0,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "5 1 0 2\nr p A 4 1 ] V C . s c [ 1 v 0 1 4 3 0 . 4 0 5 1 : v i X r a\n# Simultaneous Feature Learning and Hash Coding with Deep Neural Networks\nHanjiang Laiâ , Yan Pan ââ¡, Ye Liu§ , and Shuicheng Yanâ \nâ Department of Electronic and Computer Engineering, National University of Singapore, Singapore â¡School of Software, Sun Yan-Sen University, China § School of Information Science and Technology, Sun Yan-Sen University, China\n# Abstract",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 1 |
# Abstract
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is ï¬rst encoded as a vector of hand-engineering visual fea- tures, followed by another separate projection or quantiza- tion step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution lay- ers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate im- age features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to character- ize that one image is more similar to the second image than to the third one. Extensive evaluations on several bench- mark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substan- tial improvements over other state-of-the-art supervised or unsupervised hashing methods.
# 1. Introduction
|
1504.03410#1
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 1,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "# Abstract\nSimilarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is ï¬rst encoded as a vector of hand-engineering visual fea- tures, followed by another separate projection or quantiza- tion step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution lay- ers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate im- age features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to character- ize that one image is more similar to the second image than to the third one. Extensive evaluations on several bench- mark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substan- tial improvements over other state-of-the-art supervised or unsupervised hashing methods.\n# 1. Introduction",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 2 |
# 1. Introduction
With the ever-growing large-scale image data on the Web, much attention has been devoted to nearest neigh- bor search via hashing methods. In this paper, we focus on learning-based hashing, an emerging stream of hash meth- ods that learn similarity-preserving hash functions to en- code input data points (e.g., images) into binary codes.
Many learning-based hashing methods have been proâCorresponding author: Yan Pan, email: [email protected].
posed, e.g., [8, 9, 4, 12, 16, 27, 14, 25, 3]. The existing learning-based hashing methods can be categorized into un- supervised and supervised methods, based on whether su- pervised information (e.g., similarities or dissimilarities on data points) is involved. Compact bitwise representations are advantageous for improving the efï¬ciency in both stor- age and search speed, particularly in big data applications. Compared to unsupervised methods, supervised methods usually embed the input data points into compact hash codes with fewer bits, with the help of supervised information.
|
1504.03410#2
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 2,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "# 1. Introduction\nWith the ever-growing large-scale image data on the Web, much attention has been devoted to nearest neigh- bor search via hashing methods. In this paper, we focus on learning-based hashing, an emerging stream of hash meth- ods that learn similarity-preserving hash functions to en- code input data points (e.g., images) into binary codes.\nMany learning-based hashing methods have been proâCorresponding author: Yan Pan, email: [email protected].\nposed, e.g., [8, 9, 4, 12, 16, 27, 14, 25, 3]. The existing learning-based hashing methods can be categorized into un- supervised and supervised methods, based on whether su- pervised information (e.g., similarities or dissimilarities on data points) is involved. Compact bitwise representations are advantageous for improving the efï¬ciency in both stor- age and search speed, particularly in big data applications. Compared to unsupervised methods, supervised methods usually embed the input data points into compact hash codes with fewer bits, with the help of supervised information.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 3 |
In the pipelines of most existing hashing methods for im- ages, each input image is ï¬rstly represented by a vector of traditional hand-crafted visual descriptors (e.g., GIST [18], HOG [1]), followed by separate projection and quantiza- tion steps to encode this vector into a binary code. How- ever, such ï¬xed hand-crafted visual features may not be op- timally compatible with the coding process. In other words, a pair of semantically similar/dissimilar images may not have feature vectors with relatively small/large Euclidean distance. Ideally, it is expected that an image feature rep- resentation can sufï¬ciently preserve the image similarities, which can be learned during the hash learning process. Very recently, Xia et al. [27] proposed CNNH, a supervised hash- ing method in which the learning process is decomposed into a stage of learning approximate hash codes from the su- pervised information, followed by a stage of simultaneously learning hash functions and image representations based on the learned approximate hash codes. However, in this two-stage method, the learned approximate hash codes are used to guide the learning of the image representation, but the learned image representation cannot give feedback for learning better approximate hash codes. This one-way in- teraction thus still has limitations.
|
1504.03410#3
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 3,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "In the pipelines of most existing hashing methods for im- ages, each input image is ï¬rstly represented by a vector of traditional hand-crafted visual descriptors (e.g., GIST [18], HOG [1]), followed by separate projection and quantiza- tion steps to encode this vector into a binary code. How- ever, such ï¬xed hand-crafted visual features may not be op- timally compatible with the coding process. In other words, a pair of semantically similar/dissimilar images may not have feature vectors with relatively small/large Euclidean distance. Ideally, it is expected that an image feature rep- resentation can sufï¬ciently preserve the image similarities, which can be learned during the hash learning process. Very recently, Xia et al. [27] proposed CNNH, a supervised hash- ing method in which the learning process is decomposed into a stage of learning approximate hash codes from the su- pervised information, followed by a stage of simultaneously learning hash functions and image representations based on the learned approximate hash codes. However, in this two-stage method, the learned approximate hash codes are used to guide the learning of the image representation, but the learned image representation cannot give feedback for learning better approximate hash codes. This one-way in- teraction thus still has limitations.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 4 |
In this paper, we propose a âone-stageâ supervised hash- ing method via a deep architecture that maps input images to binary codes. As shown in Figure 1, the proposed deep architecture has three building blocks: 1) shared stacked
1
# Figure (I, 1*, image feature
ranking
pairs and maximize those on dissimilar pairs.
# i.e., the
# image triplet
convolution layers to capture a useful image representation, 2) divide-and-encode modules to divide intermediate im- age features into multiple branches, with each branch cor- responding to one hash bit, (3) a triplet ranking loss [17] designed to preserve relative similarities. Extensive evalua- tions on several benchmarks show that the proposed deep- networks-based hashing method has substantially superior search accuracies over the state-of-the-art supervised or un- supervised hashing methods.
# 2. Related Work
Learning-based hashing methods can be divided into two categories: unsupervised methods and supervised methods. Unsupervised methods only use the training data to learn hash functions that can encode input data points to bi- nary codes. Notable examples in this category include Kernelized Locality-Sensitive Hashing [9], Semantic Hash- ing [19], graph-based hashing methods [26, 13], and Itera- tive Quantization [4].
|
1504.03410#4
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 4,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "In this paper, we propose a âone-stageâ supervised hash- ing method via a deep architecture that maps input images to binary codes. As shown in Figure 1, the proposed deep architecture has three building blocks: 1) shared stacked\n1\n# Figure (I, 1*, image feature\nranking\npairs and maximize those on dissimilar pairs. \n# i.e., the\n# image triplet\nconvolution layers to capture a useful image representation, 2) divide-and-encode modules to divide intermediate im- age features into multiple branches, with each branch cor- responding to one hash bit, (3) a triplet ranking loss [17] designed to preserve relative similarities. Extensive evalua- tions on several benchmarks show that the proposed deep- networks-based hashing method has substantially superior search accuracies over the state-of-the-art supervised or un- supervised hashing methods.\n# 2. Related Work\nLearning-based hashing methods can be divided into two categories: unsupervised methods and supervised methods. Unsupervised methods only use the training data to learn hash functions that can encode input data points to bi- nary codes. Notable examples in this category include Kernelized Locality-Sensitive Hashing [9], Semantic Hash- ing [19], graph-based hashing methods [26, 13], and Itera- tive Quantization [4].",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 5 |
In most of the existing supervised hashing methods for images, input images are represented by some hand-crafted visual features (e.g. GIST [18]), before the projection and quantization steps to generate hash codes.
On the other hand, we are witnessing dramatic progress in deep convolution networks in the last few years. Ap- proaches based on deep networks have achieved state-of- the-art performance on image classiï¬cation [7, 21, 23], object detection [7, 23] and other recognition tasks [24]. The recent trend in convolution networks has been to in- crease the depth of the networks [11, 21, 23] and the layer size [20, 23]. The success of deep-networks-based meth- ods for images is mainly due to their power of automati- cally learning effective image representations. In this paper, we focus on a deep architecture tailored for learning-based hashing. Some parts of the proposed architecture are de- signed on the basis of [11] that uses additional 1 à 1 con- volution layers to increase the representational power of the networks.
|
1504.03410#5
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 5,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "In most of the existing supervised hashing methods for images, input images are represented by some hand-crafted visual features (e.g. GIST [18]), before the projection and quantization steps to generate hash codes.\nOn the other hand, we are witnessing dramatic progress in deep convolution networks in the last few years. Ap- proaches based on deep networks have achieved state-of- the-art performance on image classiï¬cation [7, 21, 23], object detection [7, 23] and other recognition tasks [24]. The recent trend in convolution networks has been to in- crease the depth of the networks [11, 21, 23] and the layer size [20, 23]. The success of deep-networks-based meth- ods for images is mainly due to their power of automati- cally learning effective image representations. In this paper, we focus on a deep architecture tailored for learning-based hashing. Some parts of the proposed architecture are de- signed on the basis of [11] that uses additional 1 à 1 con- volution layers to increase the representational power of the networks.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 6 |
Supervised methods try to leverage supervised informa- tion (e.g., class labels, pairwise similarities, or relative sim- ilarities of data points) to learn compact bitwise representa- tions. Here are some representative examples in this cate- gory. Binary Reconstruction Embedding (BRE) [8] learns hash functions by minimizing the reconstruction errors be- tween the distances of data points and those of the corre- sponding hash codes. Minimal Loss Hashing (MLH) [16] and its extension [17] learn hash codes by minimizing hinge-like loss functions based on similarities or relative similarities of data points. Supervised Hashing with Kernels (KSH) [12] is a kernel-based method that pursues compact binary codes to minimize the Hamming distances on similar
|
1504.03410#6
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 6,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "Supervised methods try to leverage supervised informa- tion (e.g., class labels, pairwise similarities, or relative sim- ilarities of data points) to learn compact bitwise representa- tions. Here are some representative examples in this cate- gory. Binary Reconstruction Embedding (BRE) [8] learns hash functions by minimizing the reconstruction errors be- tween the distances of data points and those of the corre- sponding hash codes. Minimal Loss Hashing (MLH) [16] and its extension [17] learn hash codes by minimizing hinge-like loss functions based on similarities or relative similarities of data points. Supervised Hashing with Kernels (KSH) [12] is a kernel-based method that pursues compact binary codes to minimize the Hamming distances on similar",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 7 |
Without using hand-crafted image features, the recently proposed CNNH [27] decomposes the hash learning pro- cess into a stage of learning approximate hash codes, fol- lowed by a deep-networks-based stage of simultaneously learning image features and hash functions, with the raw image pixels as input. However, a limitation in CNNH is that the learned image representation (in Stage 2) cannot be used to improve the learning of approximate hash codes, although the learned approximate hash codes can be used to guide the learning of image representation. In the pro- posed method, we learn the image representation and the hash codes in one stage, such that these two tasks have interaction and help each other forward.
# 3. The Proposed Approach
|
1504.03410#7
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 7,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "Without using hand-crafted image features, the recently proposed CNNH [27] decomposes the hash learning pro- cess into a stage of learning approximate hash codes, fol- lowed by a deep-networks-based stage of simultaneously learning image features and hash functions, with the raw image pixels as input. However, a limitation in CNNH is that the learned image representation (in Stage 2) cannot be used to improve the learning of approximate hash codes, although the learned approximate hash codes can be used to guide the learning of image representation. In the pro- posed method, we learn the image representation and the hash codes in one stage, such that these two tasks have interaction and help each other forward.\n# 3. The Proposed Approach",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 8 |
# 3. The Proposed Approach
We assume I to be the image space. The goal of hash learning for images is to learn a mapping F : I â {0, 1}q 1, such that an input image I can be encoded into a q-bit binary code F(I), with the similarities of images being preserved. In this paper, we propose an architecture of deep con- volution networks designed for hash learning, as shown in Figure 1. This architecture accepts input images in a triplet form. Given triplets of input images, the pipeline of the pro- posed architecture contains three parts: 1) a sub-network with multiple convolution-pooling layers to capture a rep- resentation of images; 2) a divide-and-encode module de- signed to generate bitwise hash codes; 3) a triplet ranking loss layer for learning good similarity measures. In the fol- lowing, we will present the details of these parts, respec- tively.
# 3.1. Triplet Ranking Loss and Optimization
|
1504.03410#8
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 8,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "# 3. The Proposed Approach\nWe assume I to be the image space. The goal of hash learning for images is to learn a mapping F : I â {0, 1}q 1, such that an input image I can be encoded into a q-bit binary code F(I), with the similarities of images being preserved. In this paper, we propose an architecture of deep con- volution networks designed for hash learning, as shown in Figure 1. This architecture accepts input images in a triplet form. Given triplets of input images, the pipeline of the pro- posed architecture contains three parts: 1) a sub-network with multiple convolution-pooling layers to capture a rep- resentation of images; 2) a divide-and-encode module de- signed to generate bitwise hash codes; 3) a triplet ranking loss layer for learning good similarity measures. In the fol- lowing, we will present the details of these parts, respec- tively.\n# 3.1. Triplet Ranking Loss and Optimization",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 9 |
# 3.1. Triplet Ranking Loss and Optimization
In most of the existing supervised hashing methods, the side information is in the form of pairwise labels that indi- cate the semantical similarites/dissimilarites on image pairs. The loss functions in these methods are thus designed to preserve the pairwise similarities of images. Recently, some efforts [17, 10] have been made to learn hash functions that preserve relative similarities of the form âimage I is more similar to image I + than to image I ââ. Such a form of triplet-based relative similarities can be more easily ob- tained than pairwise similarities (e.g., the click-through data from image retrieval systems). Furthermore, given the side information of pairwise similarities, one can easily generate a set of triplet constraints2.
|
1504.03410#9
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 9,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "# 3.1. Triplet Ranking Loss and Optimization\nIn most of the existing supervised hashing methods, the side information is in the form of pairwise labels that indi- cate the semantical similarites/dissimilarites on image pairs. The loss functions in these methods are thus designed to preserve the pairwise similarities of images. Recently, some efforts [17, 10] have been made to learn hash functions that preserve relative similarities of the form âimage I is more similar to image I + than to image I ââ. Such a form of triplet-based relative similarities can be more easily ob- tained than pairwise similarities (e.g., the click-through data from image retrieval systems). Furthermore, given the side information of pairwise similarities, one can easily generate a set of triplet constraints2.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 10 |
In the proposed deep architecture, we propose to use a variant of the triplet ranking loss in [17] to preserve the rel- ative similarities of images. Speciï¬cally, given the training triplets of images in the form of (I, I +, I â) in which I is more similar to I + than to I â, the goal is to ï¬nd a mapping F(.) such that the binary code F(I) is closer to F(I +) than to F(I â). Accordingly, the triplet ranking hinge loss is de- ï¬ned by
lrripter(F(I), FI"), F(L)) =max(0,1 = (\[F(D) â F(Z ile â FD) â FU") h)) st. F(I), F(I*), F(I7) ⬠{0,1}4, (d)
1In some of the existing hash methods, e.g., [27, 12], this mapping (or the set of hash functions) is deï¬ned as F : I â {â1, 1}q, which is essentially the same as the deï¬nition used here.
|
1504.03410#10
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 10,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "In the proposed deep architecture, we propose to use a variant of the triplet ranking loss in [17] to preserve the rel- ative similarities of images. Speciï¬cally, given the training triplets of images in the form of (I, I +, I â) in which I is more similar to I + than to I â, the goal is to ï¬nd a mapping F(.) such that the binary code F(I) is closer to F(I +) than to F(I â). Accordingly, the triplet ranking hinge loss is de- ï¬ned by\nlrripter(F(I), FI\"), F(L)) =max(0,1 = (\\[F(D) â F(Z ile â FD) â FU\") h)) st. F(I), F(I*), F(I7) ⬠{0,1}4, (d)\n1In some of the existing hash methods, e.g., [27, 12], this mapping (or the set of hash functions) is deï¬ned as F : I â {â1, 1}q, which is essentially the same as the deï¬nition used here.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 11 |
2For example, for a pair of similar images (I1, I2) and a pair of dissim- ilar images (I1, I3), one can generate a triplet (I1, I2, I3) that represents âimage I1 is more similar to image I2 than to image I3â.
where ||.||7, represents the Hamming distance. For ease of optimization, natural relaxation tricks on (1) are to replace the Hamming norm with the ¢2 norm and replace the in- teger constraints on F(.) with the range constraints. The modified loss functions is
Crripter(F (I), F(I*), FL) =max(0, ||F(2) â FI*)||3 â ||F) â FU )|I3 + 1) s.t. F(I), F(I*+), FUI-) ⬠[0, 1%. (2)
This variant of triplet ranking loss is convex. )gradients with respect to F(I), F(I +) or F(I â) are
|
1504.03410#11
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 11,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "2For example, for a pair of similar images (I1, I2) and a pair of dissim- ilar images (I1, I3), one can generate a triplet (I1, I2, I3) that represents âimage I1 is more similar to image I2 than to image I3â.\nwhere ||.||7, represents the Hamming distance. For ease of optimization, natural relaxation tricks on (1) are to replace the Hamming norm with the ¢2 norm and replace the in- teger constraints on F(.) with the range constraints. The modified loss functions is\nCrripter(F (I), F(I*), FL) =max(0, ||F(2) â FI*)||3 â ||F) â FU )|I3 + 1) s.t. F(I), F(I*+), FUI-) ⬠[0, 1%. (2)\nThis variant of triplet ranking loss is convex. )gradients with respect to F(I), F(I +) or F(I â) are",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 12 |
This variant of triplet ranking loss is convex. )gradients with respect to F(I), F(I +) or F(I â) are
oe a = (2b7 = 2b*) x Tipo |j2â| âv- ||24150 oe apr = (20% = 2b) x Tip-v+ igo ig+1>0 G) oe _ db (2b7 â 2b) x Typ_o+||2â|Jo-0-|[3-41 50°
where we denote F(I), F(I +), F(I â) as b, b+, bâ. The indicator function Icondition = 1 if condition is true; oth- erwise Icondition = 0. Hence, the loss function in (2) can be easily integrated in back propagation in neural networks.
# 3.2. Shared Sub-Network with Stacked Convolution Layers
|
1504.03410#12
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 12,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "This variant of triplet ranking loss is convex. )gradients with respect to F(I), F(I +) or F(I â) are\noe a = (2b7 = 2b*) x Tipo |j2â|\u000bâv- ||24150 oe apr = (20% = 2b) x Tip-v+ igo ig+1>0 G) oe _ db (2b7 â 2b) x Typ_o+||2â|Jo-0-|[3-41 50°\nwhere we denote F(I), F(I +), F(I â) as b, b+, bâ. The indicator function Icondition = 1 if condition is true; oth- erwise Icondition = 0. Hence, the loss function in (2) can be easily integrated in back propagation in neural networks.\n# 3.2. Shared Sub-Network with Stacked Convolution Layers",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 13 |
# 3.2. Shared Sub-Network with Stacked Convolution Layers
With this modiï¬ed triplet ranking loss function (2), the input to the proposed deep architecture are triplets of im- ages, i.e., {(Ii, I + i , I â i=1, in which Ii is more similar to than to I â I + (i = 1, 2, ...n). As shown in Figure 1, we i i propose to use a shared sub-network with a stack of convo- lution layers to automatically learn a uniï¬ed representation of the input images. Through this sub-network, an input triplet (I, I +, I â) is encoded to a triplet of intermediate im- age features (x, x+, xâ), where x, x+, xâ are vectors with the same dimension.
|
1504.03410#13
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 13,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "# 3.2. Shared Sub-Network with Stacked Convolution Layers\nWith this modiï¬ed triplet ranking loss function (2), the input to the proposed deep architecture are triplets of im- ages, i.e., {(Ii, I + i , I â i=1, in which Ii is more similar to than to I â I + (i = 1, 2, ...n). As shown in Figure 1, we i i propose to use a shared sub-network with a stack of convo- lution layers to automatically learn a uniï¬ed representation of the input images. Through this sub-network, an input triplet (I, I +, I â) is encoded to a triplet of intermediate im- age features (x, x+, xâ), where x, x+, xâ are vectors with the same dimension.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 14 |
In this sub-network, we adopt the architecture of Net- work in Network [11] as our basic framework, where we insert convolution layers with 1 à 1 ï¬lters after some con- volution layers with ï¬lters of a larger receptive ï¬eld. These 1 à 1 convolution ï¬lters can be regarded as a linear trans- formation of their input channels (followed by rectiï¬cation non-linearity). As suggested in [11], we use an average- pooling layer as the output layer of this sub-network, to re- place the fully-connected layer(s) used in traditional archi- tectures (e.g., [7]). As an example, Table 1 shows the con- ï¬gurations of the sub-network for images of size 256 à 256. Note that all the convolution layers use rectiï¬cation activa- tion which are omitted in Table 1.
This sub-network is shared by the three images in each input triplet. Such a way of parameter sharing can signif- icantly reduce the number of parameters in the whole ar- chitecture. A possible alternative is that, for (I, I +, I â)
|
1504.03410#14
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 14,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "In this sub-network, we adopt the architecture of Net- work in Network [11] as our basic framework, where we insert convolution layers with 1 à 1 ï¬lters after some con- volution layers with ï¬lters of a larger receptive ï¬eld. These 1 à 1 convolution ï¬lters can be regarded as a linear trans- formation of their input channels (followed by rectiï¬cation non-linearity). As suggested in [11], we use an average- pooling layer as the output layer of this sub-network, to re- place the fully-connected layer(s) used in traditional archi- tectures (e.g., [7]). As an example, Table 1 shows the con- ï¬gurations of the sub-network for images of size 256 à 256. Note that all the convolution layers use rectiï¬cation activa- tion which are omitted in Table 1.\nThis sub-network is shared by the three images in each input triplet. Such a way of parameter sharing can signif- icantly reduce the number of parameters in the whole ar- chitecture. A possible alternative is that, for (I, I +, I â)",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 15 |
Table 1. Conï¬gurations of the shared sub-network for input images of size 256 à 256 type convolution convolution max pool convolution convolution max pool convolution convolution max pool convolution convolution ave pool
in a triplet, the query I has an independent sub-network P , while I + and I â have a shared sub-network Q, where P /Q maps I/(I +, I â) into the corresponding image feature vector(s) (i.e., x, x+ and xâ, respectively)3. The scheme of such an alternative is similar to the idea of âasymmetric hashingâ methods [15], which use two distinct hash coding maps on a pair of images. In our experiments, we empir- ically show that a shared sub-network of capturing a uni- ï¬ed image representation performs better than the alterna- tive with two independent sub-networks.
# 3.3. Divide-and-Encode Module
|
1504.03410#15
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 15,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "Table 1. Conï¬gurations of the shared sub-network for input images of size 256 à 256 type convolution convolution max pool convolution convolution max pool convolution convolution max pool convolution convolution ave pool\nin a triplet, the query I has an independent sub-network P , while I + and I â have a shared sub-network Q, where P /Q maps I/(I +, I â) into the corresponding image feature vector(s) (i.e., x, x+ and xâ, respectively)3. The scheme of such an alternative is similar to the idea of âasymmetric hashingâ methods [15], which use two distinct hash coding maps on a pair of images. In our experiments, we empir- ically show that a shared sub-network of capturing a uni- ï¬ed image representation performs better than the alterna- tive with two independent sub-networks.\n# 3.3. Divide-and-Encode Module",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 16 |
# 3.3. Divide-and-Encode Module
After obtaining intermediate image features from the shared sub-network with stacked convolution layers, we propose a divide-and-encode module to map these image features to approximate hash codes. We assume each target hash code has q bits. Then the outputs of the shared sub- network are designed to be 50q (see the output size of the average-pooling layer in Table 1). As can be seen in Fig- ure 2(a), the proposed divide-and-encode module ï¬rstly di- vides the input intermediate features into q slices with equal length4. Then each slice is mapped to one dimension by a fully-connected layer, followed by a sigmoid activation function that restricts the output value in the range [0, 1],
|
1504.03410#16
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 16,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "# 3.3. Divide-and-Encode Module\nAfter obtaining intermediate image features from the shared sub-network with stacked convolution layers, we propose a divide-and-encode module to map these image features to approximate hash codes. We assume each target hash code has q bits. Then the outputs of the shared sub- network are designed to be 50q (see the output size of the average-pooling layer in Table 1). As can be seen in Fig- ure 2(a), the proposed divide-and-encode module ï¬rstly di- vides the input intermediate features into q slices with equal length4. Then each slice is mapped to one dimension by a fully-connected layer, followed by a sigmoid activation function that restricts the output value in the range [0, 1],",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 17 |
3Another possible alternative is that each of I, I + and I â in a triplet has an independent sub-networks (i.e., a sub-network P /Q/R corresponds to I/I +/I â, respectively), which maps it into corresponding intermediate image features. However, such an alternative tends to get bad solutions. An extreme example is, for any input triplets, the sub-network P outputs hash codes with all zeros; the sub-network Q also outputs hash codes with all zeros; the sub-network R outputs hash codes with all ones. Such kind of solutions may have zero loss on training data, but their generalization performances (on test data) can be very bad. Hence, in order to avoid such bad solutions, we consider the alternative that uses a shared sub-network for I + and I â (i.e., let Q = R).
4For ease of presentation, here we assume the dimension d of the input intermediate image features is a multiple of q. In practice, if d = q à s + c with 0 < c < q, we can set the ï¬rst c slices to be length of s + 1 and the rest q â c ones to be length of s.
|
1504.03410#17
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 17,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "3Another possible alternative is that each of I, I + and I â in a triplet has an independent sub-networks (i.e., a sub-network P /Q/R corresponds to I/I +/I â, respectively), which maps it into corresponding intermediate image features. However, such an alternative tends to get bad solutions. An extreme example is, for any input triplets, the sub-network P outputs hash codes with all zeros; the sub-network Q also outputs hash codes with all zeros; the sub-network R outputs hash codes with all ones. Such kind of solutions may have zero loss on training data, but their generalization performances (on test data) can be very bad. Hence, in order to avoid such bad solutions, we consider the alternative that uses a shared sub-network for I + and I â (i.e., let Q = R).\n4For ease of presentation, here we assume the dimension d of the input intermediate image features is a multiple of q. In practice, if d = q à s + c with 0 < c < q, we can set the ï¬rst c slices to be length of s + 1 and the rest q â c ones to be length of s.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 18 |
fully connected fully connected @ eo o\ LSP coum 8 sigmoit jiece-wise sigmoid o| req mee | 8) @|=| e+e | ed + @ @ @] r@| | ex -O | [Sisfel-[s| /Saeeecs e| Le 1?! | oA re e| |e / of ) ee" % @ eo (a) divide-and-encode module (b) fully-connected alternation
Figure 2. (a) A divide-and-encode module. (b) An alternative that consists of a fully-connected layer, followed by a sigmoid layer.
and a piece-wise threshold function to encourage the output of binary hash bits. After that, the q output hash bits are concatenated to be a q-bit (approximate) code.
|
1504.03410#18
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 18,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "fully connected fully connected @ eo o\\ LSP coum 8 sigmoit jiece-wise sigmoid o| req mee | 8) @|=| e+e | ed + @ @ @] r@| | ex -O | [Sisfel-[s| /Saeeecs e| Le 1?! | oA re e| |e / of ) ee\" % @ eo (a) divide-and-encode module (b) fully-connected alternation\nFigure 2. (a) A divide-and-encode module. (b) An alternative that consists of a fully-connected layer, followed by a sigmoid layer.\nand a piece-wise threshold function to encourage the output of binary hash bits. After that, the q output hash bits are concatenated to be a q-bit (approximate) code.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 19 |
As shown in Figure 2(b), a possible alternative to the divide-and-encode module is a simple fully-connected layer that maps the input intermediate image features into q- dimensional vectors, followed by sigmoid activation func- tions to transform these vectors into [0, 1]q. Compared to this alternative, the key idea of the overall divide-and- encode strategy is trying to reduce the redundancy among the hash bits. Speciï¬cally, in the fully-connected alterna- tive in Figure 2(b), each hash bit is generated on the ba- sis of the whole (and the same) input image feature vec- tor, which may inevitably result in redundancy among the hash bits. On the other hand, since each hash bit is gen- erated from a separated slice of features, the output hash codes from the proposed divide-and-encode module may be less redundant to each other. Hash codes with fewer redun- dant bits are advocated by some recent research. For exam- ple, the recently proposed Batch-Orthogonal Locality Sen- sitive Hashing [5] theoretically and empirically shows that hash codes generated by batch-orthogonalized random pro- jections are superior to those generated by simple
|
1504.03410#19
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 19,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "As shown in Figure 2(b), a possible alternative to the divide-and-encode module is a simple fully-connected layer that maps the input intermediate image features into q- dimensional vectors, followed by sigmoid activation func- tions to transform these vectors into [0, 1]q. Compared to this alternative, the key idea of the overall divide-and- encode strategy is trying to reduce the redundancy among the hash bits. Speciï¬cally, in the fully-connected alterna- tive in Figure 2(b), each hash bit is generated on the ba- sis of the whole (and the same) input image feature vec- tor, which may inevitably result in redundancy among the hash bits. On the other hand, since each hash bit is gen- erated from a separated slice of features, the output hash codes from the proposed divide-and-encode module may be less redundant to each other. Hash codes with fewer redun- dant bits are advocated by some recent research. For exam- ple, the recently proposed Batch-Orthogonal Locality Sen- sitive Hashing [5] theoretically and empirically shows that hash codes generated by batch-orthogonalized random pro- jections are superior to those generated by simple",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
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] |
1504.03410
| 20 |
sitive Hashing [5] theoretically and empirically shows that hash codes generated by batch-orthogonalized random pro- jections are superior to those generated by simple random projections, where batch-orthogonalized projections gener- ate fewer redundant hash bits than random projections. In the experiments section, we empirically show that the pro- posed divide-and-encode module leads to superior perfor- mance over the fully-connected alternative.
|
1504.03410#20
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 20,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "sitive Hashing [5] theoretically and empirically shows that hash codes generated by batch-orthogonalized random pro- jections are superior to those generated by simple random projections, where batch-orthogonalized projections gener- ate fewer redundant hash bits than random projections. In the experiments section, we empirically show that the pro- posed divide-and-encode module leads to superior perfor- mance over the fully-connected alternative.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 21 |
In order to encourage the output of a divide-and-encode module to be binary codes, we use a sigmoid activa- tion function followed by a piece-wise threshold function. Given a 50-dimensional slice x(i)(i = 1, 2, ..., q), the out- put of the 50-to-1 fully-connected layer is deï¬ned by
f ci(x(i)) = Wix(i), (4)
with Wi being the weight matrix.
0.5 0 0.5 1
Figure 3. The piece-wise threshold function.
Given c = f ci(x(i)), the sigmoid function is deï¬ned by
sigmoid(c) = 1 1 + eâβc , (5)
where β is a hyper-parameter.
The piece-wise threshold function, as shown in Figure 3, is to encourage binary outputs. Speciï¬cally, for an input variable s = sigmoid(c) â [0, 1], this piece-wise function is deï¬ned by
0, s<05-⬠g(s) = s, 05-e<s<05+e (6) 1, s>0.5+e,
where ⬠is a small positive hyper-parameter.
|
1504.03410#21
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 21,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "In order to encourage the output of a divide-and-encode module to be binary codes, we use a sigmoid activa- tion function followed by a piece-wise threshold function. Given a 50-dimensional slice x(i)(i = 1, 2, ..., q), the out- put of the 50-to-1 fully-connected layer is deï¬ned by\nf ci(x(i)) = Wix(i), (4)\nwith Wi being the weight matrix.\n0.5 0 0.5 1\nFigure 3. The piece-wise threshold function.\nGiven c = f ci(x(i)), the sigmoid function is deï¬ned by\nsigmoid(c) = 1 1 + eâβc , (5)\nwhere β is a hyper-parameter.\nThe piece-wise threshold function, as shown in Figure 3, is to encourage binary outputs. Speciï¬cally, for an input variable s = sigmoid(c) â [0, 1], this piece-wise function is deï¬ned by\n0, s<05-⬠g(s) = s, 05-e<s<05+e (6) 1, s>0.5+e,\nwhere ⬠is a small positive hyper-parameter.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 22 |
where ⬠is a small positive hyper-parameter.
This piece-wise threshold function approximates the be- havior of hard-coding, and it encourages binary outputs in training. Specifically, if the outputs from the sigmoid func- tion are in [0,0.5 â e) or (0.5 + e, 1], they are truncated to be 0 or 1, respectively. Note that in prediction, the pro- posed deep architecture only generates approximate (real- value) hash codes for input images, where these approxi- mate codes are converted to binary codes by quantization (see Section 3.4 for details). With the proposed piece-wise threshold function, some of the values in the approximate hash codes (that are produced by the deep architecture) are already zeros or ones. Hence, less errors may be introduced by the quantization step.
# 3.4. Hash Coding for New Images
|
1504.03410#22
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 22,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "where ⬠is a small positive hyper-parameter.\nThis piece-wise threshold function approximates the be- havior of hard-coding, and it encourages binary outputs in training. Specifically, if the outputs from the sigmoid func- tion are in [0,0.5 â e) or (0.5 + e, 1], they are truncated to be 0 or 1, respectively. Note that in prediction, the pro- posed deep architecture only generates approximate (real- value) hash codes for input images, where these approxi- mate codes are converted to binary codes by quantization (see Section 3.4 for details). With the proposed piece-wise threshold function, some of the values in the approximate hash codes (that are produced by the deep architecture) are already zeros or ones. Hence, less errors may be introduced by the quantization step.\n# 3.4. Hash Coding for New Images",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 23 |
# 3.4. Hash Coding for New Images
After the deep architecture is trained, one can use it to generate a q-bit hash code for an input image. As shown in Figure 4, in prediction, an input image I is ï¬rst en- coded into a q-dimensional feature vector F(I). Then one can obtain a q-bit binary code by simple quantization b = sign(F(I) â 0.5), where sign(v) is the sign function on vectors that for i = 1, 2, ..., q, sign(vi) = 1 if vi > 0, otherwise sign(vi) = 0.
divide-and-encode quantization image shared sub network et, e kes 0 helo a1 âeo 0 of el
Figure 4. The architecture of prediction.
# 4. Experiments
# 4.1. Experimental Settings
In this section, we conduct extensive evaluations of the proposed method on three benchmark datasets:
⢠The Stree View House Numbers (SVHN)5 dataset is a real-world image dataset for recognizing digits and numbers in natural scene images. SVHN consists of over 600,000 32 à 32 color images in 10 classes (with digits from 0 to 9).
⢠The CIFAR-106 dataset consists of 60,000 color im- ages in 10 classes. Each class has 6,000 images in size 32 à 32.
|
1504.03410#23
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 23,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "# 3.4. Hash Coding for New Images\nAfter the deep architecture is trained, one can use it to generate a q-bit hash code for an input image. As shown in Figure 4, in prediction, an input image I is ï¬rst en- coded into a q-dimensional feature vector F(I). Then one can obtain a q-bit binary code by simple quantization b = sign(F(I) â 0.5), where sign(v) is the sign function on vectors that for i = 1, 2, ..., q, sign(vi) = 1 if vi > 0, otherwise sign(vi) = 0.\ndivide-and-encode quantization image shared sub network et, e kes 0 helo a1 âeo 0 of el\nFigure 4. The architecture of prediction.\n# 4. Experiments\n# 4.1. Experimental Settings\nIn this section, we conduct extensive evaluations of the proposed method on three benchmark datasets:\n⢠The Stree View House Numbers (SVHN)5 dataset is a real-world image dataset for recognizing digits and numbers in natural scene images. SVHN consists of over 600,000 32 à 32 color images in 10 classes (with digits from 0 to 9).\n⢠The CIFAR-106 dataset consists of 60,000 color im- ages in 10 classes. Each class has 6,000 images in size 32 à 32.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 24 |
⢠The CIFAR-106 dataset consists of 60,000 color im- ages in 10 classes. Each class has 6,000 images in size 32 à 32.
⢠The NUS-WIDE7 dataset contains nearly 270,000 im- ages collected from Flickr. Each of these images is associated with one or multiple labels in 81 semantic concepts. For a fair comparison, we follow the set- tings in [27, 13] to use the subset of images associated with the 21 most frequent labels, where each label as- sociates with at least 5,000 images. We resize images of this subset into 256 à 256.
We test and compare the search accuracies of the pro- posed method with eight state-of-the-art hashing methods, including three unsupervised methods LSH [2], SH [26] and ITQ [4], and ï¬ve supervised methods CNNH [27], KSH [12], MLH [16], BRE [8] and ITQ-CCA [4].
|
1504.03410#24
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 24,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "⢠The CIFAR-106 dataset consists of 60,000 color im- ages in 10 classes. Each class has 6,000 images in size 32 à 32.\n⢠The NUS-WIDE7 dataset contains nearly 270,000 im- ages collected from Flickr. Each of these images is associated with one or multiple labels in 81 semantic concepts. For a fair comparison, we follow the set- tings in [27, 13] to use the subset of images associated with the 21 most frequent labels, where each label as- sociates with at least 5,000 images. We resize images of this subset into 256 à 256.\nWe test and compare the search accuracies of the pro- posed method with eight state-of-the-art hashing methods, including three unsupervised methods LSH [2], SH [26] and ITQ [4], and ï¬ve supervised methods CNNH [27], KSH [12], MLH [16], BRE [8] and ITQ-CCA [4].",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 25 |
In SVHN and CIFAR-10, we randomly select 1,000 im- ages (100 images per class) as the test query set. For the unsupervised methods, we use the rest images as training samples. For the supervised methods, we randomly select 5,000 images (500 images per class) from the rest images as the training set. The triplets of images for training are randomly constructed based on the image class labels.
In NUS-WIDE, we randomly select 100 images from each of the selected 21 classes to form a test query set of 2,100 images. For the unsupervised methods, the rest im- ages in the selected 21 classes are used as the training set. For supervised methods, we uniformly sample 500 images from each of the selected 21 classes to form a training set.
# 5http://uï¬dl.stanford.edu/housenumbers/ 6http://www.cs.toronto.edu/ kriz/cifar.html 7http://lms.comp.nus.edu.sg/research/NUS-WIDE.htm
|
1504.03410#25
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 25,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "In SVHN and CIFAR-10, we randomly select 1,000 im- ages (100 images per class) as the test query set. For the unsupervised methods, we use the rest images as training samples. For the supervised methods, we randomly select 5,000 images (500 images per class) from the rest images as the training set. The triplets of images for training are randomly constructed based on the image class labels.\nIn NUS-WIDE, we randomly select 100 images from each of the selected 21 classes to form a test query set of 2,100 images. For the unsupervised methods, the rest im- ages in the selected 21 classes are used as the training set. For supervised methods, we uniformly sample 500 images from each of the selected 21 classes to form a training set.\n# 5http://uï¬dl.stanford.edu/housenumbers/ 6http://www.cs.toronto.edu/ kriz/cifar.html 7http://lms.comp.nus.edu.sg/research/NUS-WIDE.htm",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 26 |
Table 2. MAP of Hamming ranking w.r.t different numbers of bits on three datasets. For NUS-WIDE, we calculate the MAP values within the top 5000 returned neighbors. The results of CNNH is directly cited from [27]. CNNH«x is our implementation of the CNNH method in [27] using Caffe, by using a network configuration comparable to that of the proposed method (see the text in Section 4.1 for implementation details).
|
1504.03410#26
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 26,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "Table 2. MAP of Hamming ranking w.r.t different numbers of bits on three datasets. For NUS-WIDE, we calculate the MAP values within the top 5000 returned neighbors. The results of CNNH is directly cited from [27]. CNNH«x is our implementation of the CNNH method in [27] using Caffe, by using a network configuration comparable to that of the proposed method (see the text in Section 4.1 for implementation details).",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 27 |
Method SVHN(MAP) CIFAR-10(MAP) NUS-WIDE(MAP) 12bits 24bits 32bits 48bits | 12bits 24bits 32bits 48bits | 12bits 24bits 32 bits 48 bits Ours 0.899 0.914 0.925 0.923 | 0.552 0.566 = 0.558 (0.581 | 0.674 0.697 0.713 0.715 CNNH* 0.897 0.903 0.904 0.896 | 0.484 0.476 0.472 0489 | 0.617 0.663 0.657 0.688 CNNH [27] 0.439 (0.511 0.509 0.522 | 0.611 0.618 0.625 0.608 KSH [12] 0.469 0.539 0.563 0.581 0.303 =0.337. 0.346 = 0.356 | 0.556 0.572 0.581 0.588 ITQ-CCA [4] | 0.428 0.488 0.489 0.509 | 0.264 0.282 0.288 ~â0.295 | 0.435 0.435 0.435 0.435 MLH [16] 0.147 0.247 0.261 0.273 | 0.182 0.195 0.207 0.211 |
|
1504.03410#27
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 27,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "Method SVHN(MAP) CIFAR-10(MAP) NUS-WIDE(MAP) 12bits 24bits 32bits 48bits | 12bits 24bits 32bits 48bits | 12bits 24bits 32 bits 48 bits Ours 0.899 0.914 0.925 0.923 | 0.552 0.566 = 0.558 (0.581 | 0.674 0.697 0.713 0.715 CNNH* 0.897 0.903 0.904 0.896 | 0.484 0.476 0.472 0489 | 0.617 0.663 0.657 0.688 CNNH [27] 0.439 (0.511 0.509 0.522 | 0.611 0.618 0.625 0.608 KSH [12] 0.469 0.539 0.563 0.581 0.303 =0.337. 0.346 = 0.356 | 0.556 0.572 0.581 0.588 ITQ-CCA [4] | 0.428 0.488 0.489 0.509 | 0.264 0.282 0.288 ~â0.295 | 0.435 0.435 0.435 0.435 MLH [16] 0.147 0.247 0.261 0.273 | 0.182 0.195 0.207 0.211 |",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 28 |
0.435 0.435 0.435 MLH [16] 0.147 0.247 0.261 0.273 | 0.182 0.195 0.207 0.211 | 0.500 0.514 0.520 0.522 BRE [8] 0.165 0.206 0.230 0.237 | 0.159 0.181 0.193 0.196 | 0.485 0.525 0.530 0.544 SH [26] 0.140 0.138 = 0.141 0.140 | 0.131 0.135 0.133. --0.130 | 0.433 0.426 0.426 0.423 ITQ [4] 0.127 0.132. 0.135. 0.139: | 0.162 0.169 0.172 0.175 | 0.452 0.468 0.472 0.477 LSH [2] 0.110 0.122 0.120 0.128 | 0.121 0.126 0.120 0.120 | 0.403 0.421 0.426 0.441
|
1504.03410#28
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 28,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "0.435 0.435 0.435 MLH [16] 0.147 0.247 0.261 0.273 | 0.182 0.195 0.207 0.211 | 0.500 0.514 0.520 0.522 BRE [8] 0.165 0.206 0.230 0.237 | 0.159 0.181 0.193 0.196 | 0.485 0.525 0.530 0.544 SH [26] 0.140 0.138 = 0.141 0.140 | 0.131 0.135 0.133. --0.130 | 0.433 0.426 0.426 0.423 ITQ [4] 0.127 0.132. 0.135. 0.139: | 0.162 0.169 0.172 0.175 | 0.452 0.468 0.472 0.477 LSH [2] 0.110 0.122 0.120 0.128 | 0.121 0.126 0.120 0.120 | 0.403 0.421 0.426 0.441",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 29 |
The triplets for training are also randomly constructed based on the image class labels.
For the proposed method and CNNH, we directly use the image pixels as input. For the other baseline methods, we follow [27, 12] to represent each image in SVHN and CIFAR-10 by a 512-dimensional GIST vector; we represent each image in NUS-WIDE by a 500-dimensional bag-of- words vector 8.
To evaluate the quality of hashing, we use four evalu- ation metrics: Mean Average Precision (MAP), Precision- Recall curves, Precision curves within Hamming distance 2, and Precision curves w.r.t. different numbers of top re- turned samples. For a fair comparison, all of the methods use identical training and test sets.
# 4.2. Results of Search Accuracies
Table 2 and Figure 2â¼4 show the comparison results of search accuracies on all of the three datasets. Two observa- tions can be made from these results:
|
1504.03410#29
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 29,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "The triplets for training are also randomly constructed based on the image class labels.\nFor the proposed method and CNNH, we directly use the image pixels as input. For the other baseline methods, we follow [27, 12] to represent each image in SVHN and CIFAR-10 by a 512-dimensional GIST vector; we represent each image in NUS-WIDE by a 500-dimensional bag-of- words vector 8.\nTo evaluate the quality of hashing, we use four evalu- ation metrics: Mean Average Precision (MAP), Precision- Recall curves, Precision curves within Hamming distance 2, and Precision curves w.r.t. different numbers of top re- turned samples. For a fair comparison, all of the methods use identical training and test sets.\n# 4.2. Results of Search Accuracies\nTable 2 and Figure 2â¼4 show the comparison results of search accuracies on all of the three datasets. Two observa- tions can be made from these results:",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 30 |
Table 2 and Figure 2â¼4 show the comparison results of search accuracies on all of the three datasets. Two observa- tions can be made from these results:
(1) On all of the three datasets, the proposed method achieves substantially better search accuracies (w.r.t. MAP, precision within Hamming distance 2, precision-recall, and precision with varying size of top returned samples) than those baseline methods using traditional hand-crafted vi- sual features. For example, compared to the best competi- tor KSH, the MAP results of the proposed method indicate a relative increase of 58.8% â¼90.6.% / 61.3% â¼ 82.2 % / 21.2% â¼ 22.7% on SVHN / CIFAR-10 / NUS-WIDE, re- spectively.
We implement the proposed method based on the open- source Caffe [6] framework. In all experiments, our net- works are trained by stochastic gradient descent with 0.9 momentum [22]. We initiate ⬠in the piece-wise threshold function to be 0.5 and decrease it by 20% after every 20, 000 iterations. The mini-batch size of images is 64. The weight decay parameter is 0.0005.
|
1504.03410#30
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 30,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "Table 2 and Figure 2â¼4 show the comparison results of search accuracies on all of the three datasets. Two observa- tions can be made from these results:\n(1) On all of the three datasets, the proposed method achieves substantially better search accuracies (w.r.t. MAP, precision within Hamming distance 2, precision-recall, and precision with varying size of top returned samples) than those baseline methods using traditional hand-crafted vi- sual features. For example, compared to the best competi- tor KSH, the MAP results of the proposed method indicate a relative increase of 58.8% â¼90.6.% / 61.3% â¼ 82.2 % / 21.2% â¼ 22.7% on SVHN / CIFAR-10 / NUS-WIDE, re- spectively.\nWe implement the proposed method based on the open- source Caffe [6] framework. In all experiments, our net- works are trained by stochastic gradient descent with 0.9 momentum [22]. We initiate ⬠in the piece-wise threshold function to be 0.5 and decrease it by 20% after every 20, 000 iterations. The mini-batch size of images is 64. The weight decay parameter is 0.0005.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 31 |
The results of BRE, ITQ, ITQ-CCA, KSH, MLH and SH are obtained by the implementations provided by their authors, respectively. The results of LSH are obtained from our implementation. Since the network configura- tions of CNNH in [27] are different from those of the pro- posed method, for a fair comparison, we carefully imple- ment CNNH (referred to as CNNH«) based on Caffe, where we use the code provided by the authors of [27] to imple- ment the first stage. In the second stage of CNNHx, we use the same stack of convolution-pooling layers as in Ta- ble 1, except for modifying the size of the last convolution to bits x 1 x 1 and using an average pooling layer of size bits x 1 x 1 as the output layer.
8These bag-of-words features are available in the NUS-WIDE dataset.
|
1504.03410#31
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 31,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "The results of BRE, ITQ, ITQ-CCA, KSH, MLH and SH are obtained by the implementations provided by their authors, respectively. The results of LSH are obtained from our implementation. Since the network configura- tions of CNNH in [27] are different from those of the pro- posed method, for a fair comparison, we carefully imple- ment CNNH (referred to as CNNH«) based on Caffe, where we use the code provided by the authors of [27] to imple- ment the first stage. In the second stage of CNNHx, we use the same stack of convolution-pooling layers as in Ta- ble 1, except for modifying the size of the last convolution to bits x 1 x 1 and using an average pooling layer of size bits x 1 x 1 as the output layer.\n8These bag-of-words features are available in the NUS-WIDE dataset.",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 32 |
8These bag-of-words features are available in the NUS-WIDE dataset.
(2) In most metrics on all of the three datasets, the pro- posed method shows superior performance gains against the most related competitors CNNH and CNNHx, which are deep-networks-based two-stage methods. For example, with respect to MAP, compared to the corresponding sec- ond best competitor, the proposed method shows a relative increase of 9.6 % ~ 14.0 % / 3.9% ~ 9.2% on CIFAR-10/ NUS-WIDE, respectively. These results verify that simul- taneously learning useful representation of images and hash codes of preserving similarities can benefit each other.
# 4.3. Comparison Results of the Divide-and-Encode Module against Its Alternative
|
1504.03410#32
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 32,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "8These bag-of-words features are available in the NUS-WIDE dataset.\n(2) In most metrics on all of the three datasets, the pro- posed method shows superior performance gains against the most related competitors CNNH and CNNHx, which are deep-networks-based two-stage methods. For example, with respect to MAP, compared to the corresponding sec- ond best competitor, the proposed method shows a relative increase of 9.6 % ~ 14.0 % / 3.9% ~ 9.2% on CIFAR-10/ NUS-WIDE, respectively. These results verify that simul- taneously learning useful representation of images and hash codes of preserving similarities can benefit each other.\n# 4.3. Comparison Results of the Divide-and-Encode Module against Its Alternative",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 33 |
# 4.3. Comparison Results of the Divide-and-Encode Module against Its Alternative
A natural alternative to the divide-and-encode module is a simple fully-connected layer followed by a sigmoid layer of restricting the output valuesâ range in [0, 1] (see Figure 2(b)). To investigate the effectiveness of the divide-and°Note that, on CIFAR-10, some MAP results of CNNH« are inferior to those of CNNH [27]. This is mainly due to different network configu- rations and optimization frameworks between these two implementations. CNNH« is implemented based on Caffe [6]. But the core of the original implementation in CNNH [27] is based on Cuda-Convnet [7].
(a) (b) (c)
Figure 5. The comparison results on SVNH. (a) Precision curves within Hamming radius 2; (b) precision-recall curves of Hamming ranking with 48 bits; (c) precision curves with 48 bits w.r.t. different numbers of top returned samples.
(a) (b) (c)
|
1504.03410#33
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 33,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "# 4.3. Comparison Results of the Divide-and-Encode Module against Its Alternative\nA natural alternative to the divide-and-encode module is a simple fully-connected layer followed by a sigmoid layer of restricting the output valuesâ range in [0, 1] (see Figure 2(b)). To investigate the effectiveness of the divide-and°Note that, on CIFAR-10, some MAP results of CNNH« are inferior to those of CNNH [27]. This is mainly due to different network configu- rations and optimization frameworks between these two implementations. CNNH« is implemented based on Caffe [6]. But the core of the original implementation in CNNH [27] is based on Cuda-Convnet [7].\n(a) (b) (c) \nFigure 5. The comparison results on SVNH. (a) Precision curves within Hamming radius 2; (b) precision-recall curves of Hamming ranking with 48 bits; (c) precision curves with 48 bits w.r.t. different numbers of top returned samples.\n(a) (b) (c)",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 34 |
(a) (b) (c)
Figure 6. The comparison results on CIFAR10. (a) precision curves within Hamming radius 2; (b) precision-recall curves of Hamming ranking with 48 bits; (c) precision curves with 48 bits w.r.t. different number of top returned samples
(a) (b) (c)
Figure 7. The comparison results on NUS-WIDE. (a) precision curves within Hamming radius 2; (b) precision-recall curves of Hamming ranking with 48 bits; (c) precision curves with 48 bits w.r.t. different number of top returned samples
encode module (DEM), we implement and evaluate a deep architecture derived from the proposed one in Figure 1, by replacing the divide-and-encode module with its alternative in Figure 2(b) and keeping other layers unchanged. We refer to it as âFCâ.
As can be seen from Table 3 and Figure 8, the results of the proposed method outperform the competitor with the alternative of the divide-and-encode module. For example, the architecture with DEM achieves 0.581 accuracy with 48 bits on CIFAR-10, which indicates an improvement of 19.7% over the FC alternative. The underlying reason for
|
1504.03410#34
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 34,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "(a) (b) (c) \nFigure 6. The comparison results on CIFAR10. (a) precision curves within Hamming radius 2; (b) precision-recall curves of Hamming ranking with 48 bits; (c) precision curves with 48 bits w.r.t. different number of top returned samples\n(a) (b) (c) \nFigure 7. The comparison results on NUS-WIDE. (a) precision curves within Hamming radius 2; (b) precision-recall curves of Hamming ranking with 48 bits; (c) precision curves with 48 bits w.r.t. different number of top returned samples\nencode module (DEM), we implement and evaluate a deep architecture derived from the proposed one in Figure 1, by replacing the divide-and-encode module with its alternative in Figure 2(b) and keeping other layers unchanged. We refer to it as âFCâ.\nAs can be seen from Table 3 and Figure 8, the results of the proposed method outperform the competitor with the alternative of the divide-and-encode module. For example, the architecture with DEM achieves 0.581 accuracy with 48 bits on CIFAR-10, which indicates an improvement of 19.7% over the FC alternative. The underlying reason for",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 35 |
the improvement may be that, compared to the FC alter- native, the output hash codes from the divide-and-encode modules are less redundant to each other.
# 4.4. Comparison Results of a Shared Sub-Network against Two Independent Sub-Networks
In the proposed deep architecture, we use a shared sub- network to capture a uniï¬ed image representation for the three images in an input triplet. A possible alternative to this shared sub-network is that for a triplet (I, I +, I â), the query I has an independent sub-network P , while I +
Table 3. Comparison results of the divide-and-encode module and its fully-connected alternative on three datasets. NUS-WIDE(MAP)
SVHN(MAP) CIFAR-10(MAP) 32 bits 24 bits 0.558 0.566 0.489 0.497 Method 12 bits 0.899 0.887 24 bits 0.914 0.896 32 bits 0.925 0.909 48 bits 0.923 0.912 12 bits 0.552 0.465 48bits 0.581 0.485 12 bits 0.674 0.623 24 bits 0.697 0.673 Ours (DEM) Ours (FC) 32 bits 0.713 0.682 48 bits 0.715 0.691
|
1504.03410#35
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 35,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "the improvement may be that, compared to the FC alter- native, the output hash codes from the divide-and-encode modules are less redundant to each other.\n# 4.4. Comparison Results of a Shared Sub-Network against Two Independent Sub-Networks\nIn the proposed deep architecture, we use a shared sub- network to capture a uniï¬ed image representation for the three images in an input triplet. A possible alternative to this shared sub-network is that for a triplet (I, I +, I â), the query I has an independent sub-network P , while I +\nTable 3. Comparison results of the divide-and-encode module and its fully-connected alternative on three datasets. NUS-WIDE(MAP)\nSVHN(MAP) CIFAR-10(MAP) 32 bits 24 bits 0.558 0.566 0.489 0.497 Method 12 bits 0.899 0.887 24 bits 0.914 0.896 32 bits 0.925 0.909 48 bits 0.923 0.912 12 bits 0.552 0.465 48bits 0.581 0.485 12 bits 0.674 0.623 24 bits 0.697 0.673 Ours (DEM) Ours (FC) 32 bits 0.713 0.682 48 bits 0.715 0.691",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 36 |
(a)
(b)
(c)
Figure 8. The precision curves of divide-and-encode module versus its fully-connected alternative with 48 bits w.r.t. different number of top returned samples
and I â has a shared sub-network Q, where P /Q maps I/(I +, I â) into the corresponding image feature vector(s) (i.e., x, x+ and xâ, respectively).
We implement and compare the search accuracies of the proposed architecture with a shared sub-network to its al- ternative with two independent sub-networks. As can be seen in Table 4 and 5, the results of the proposed architec- ture outperform the competitor with the alternative with two independent sub-networks. Generally speaking, although larger networks can capture more information, it also needs more training data. The underlying reason why the architec- ture with a shared sub-network performs better than the one with two independent sub-networks may be that the training samples are not enough for networks with too much param- eters (e.g., 500 training images per class on CIFAR-10 and NUS-WIDE).
Table 4. Comparison results of a shared sub-network against two independent sub-networks on CIFAR-10. 24 bits
|
1504.03410#36
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 36,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "(a)\n(b)\n(c)\nFigure 8. The precision curves of divide-and-encode module versus its fully-connected alternative with 48 bits w.r.t. different number of top returned samples\nand I â has a shared sub-network Q, where P /Q maps I/(I +, I â) into the corresponding image feature vector(s) (i.e., x, x+ and xâ, respectively).\nWe implement and compare the search accuracies of the proposed architecture with a shared sub-network to its al- ternative with two independent sub-networks. As can be seen in Table 4 and 5, the results of the proposed architec- ture outperform the competitor with the alternative with two independent sub-networks. Generally speaking, although larger networks can capture more information, it also needs more training data. The underlying reason why the architec- ture with a shared sub-network performs better than the one with two independent sub-networks may be that the training samples are not enough for networks with too much param- eters (e.g., 500 training images per class on CIFAR-10 and NUS-WIDE).\nTable 4. Comparison results of a shared sub-network against two independent sub-networks on CIFAR-10. 24 bits",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 37 |
Table 4. Comparison results of a shared sub-network against two independent sub-networks on CIFAR-10. 24 bits
Methods 12 bits 32 bits 48 bits MAP 1-sub-network 2-sub-networks 0.558 0.477 Precision within Hamming radius 2 0.602 0.549 0.552 0.467 0.566 0.494 1-sub-network 2-sub-networks 0.527 0.450 0.615 0.564 0.581 0.515 0.625 0.588
Table 5. Comparison results of a shared sub-network against two independent sub-networks on NUSWIDE.
Methods 12 bits 24 bits 32 bits 48 bits MAP 1-sub-network 2-sub-networks 0.713 0.688 Precision within Hamming radius 2 0.710 0.696 0.674 0.640 0.697 0.686 1-sub-network 2-sub-networks 0.623 0.579 0.686 0.664 0.715 0.697 0.714 0.704
|
1504.03410#37
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 37,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "Table 4. Comparison results of a shared sub-network against two independent sub-networks on CIFAR-10. 24 bits\nMethods 12 bits 32 bits 48 bits MAP 1-sub-network 2-sub-networks 0.558 0.477 Precision within Hamming radius 2 0.602 0.549 0.552 0.467 0.566 0.494 1-sub-network 2-sub-networks 0.527 0.450 0.615 0.564 0.581 0.515 0.625 0.588\nTable 5. Comparison results of a shared sub-network against two independent sub-networks on NUSWIDE.\nMethods 12 bits 24 bits 32 bits 48 bits MAP 1-sub-network 2-sub-networks 0.713 0.688 Precision within Hamming radius 2 0.710 0.696 0.674 0.640 0.697 0.686 1-sub-network 2-sub-networks 0.623 0.579 0.686 0.664 0.715 0.697 0.714 0.704",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 38 |
ing loss designed to preserve relative similarities. Through- out the proposed deep architecture, input images are con- verted into uniï¬ed image representations via a shared sub- network of stacked convolution layers. Then, these interme- diate image representations are encoded into hash codes by divide-and-encode modules. Empirical evaluations in im- age retrieval show that the proposed method has superior performance gains over state-of-the-arts.
# Acknowledgment
This work was partially supported by Adobe Gift Fund- ing. It was also supported by the National Natural Science Foundation of China under Grants 61370021, U1401256, 61472453, Natural Science Foundation of Guangdong Province under Grant S2013010011905.
# 5. Conclusion
# References
In this paper, we developed a âone-stageâ supervised hashing method for image retrieval, which generates bitwise hash codes for images via a carefully designed deep archi- tecture. The proposed deep architecture uses a triplet rank[1] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 886â893, 2005. 1
|
1504.03410#38
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 38,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "ing loss designed to preserve relative similarities. Through- out the proposed deep architecture, input images are con- verted into uniï¬ed image representations via a shared sub- network of stacked convolution layers. Then, these interme- diate image representations are encoded into hash codes by divide-and-encode modules. Empirical evaluations in im- age retrieval show that the proposed method has superior performance gains over state-of-the-arts.\n# Acknowledgment\nThis work was partially supported by Adobe Gift Fund- ing. It was also supported by the National Natural Science Foundation of China under Grants 61370021, U1401256, 61472453, Natural Science Foundation of Guangdong Province under Grant S2013010011905.\n# 5. Conclusion\n# References\nIn this paper, we developed a âone-stageâ supervised hashing method for image retrieval, which generates bitwise hash codes for images via a carefully designed deep archi- tecture. The proposed deep architecture uses a triplet rank[1] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 886â893, 2005. 1",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 39 |
[2] A. Gionis, P. Indyk, and R. Motwani. Similarity search in In Proceedings of the Inter- high dimensions via hashing. national Conference on Very Large Data Bases, pages 518â 529, 1999. 5, 6
[3] Y. Gong, S. Kumar, H. A. Rowley, and S. Lazebnik. Learning binary codes for high-dimensional data using bilinear projec- tions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 484â491, 2013. 1
Iterative quantization: A pro- In Proceed- crustean approach to learning binary codes. ings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 817â824, 2011. 1, 2, 5, 6
[5] J. Ji, S. Yan, J. Li, G. Gao, Q. Tian, and B. Zhang. Batch- orthogonal locality-sensitive hashing for angular similarity. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 36(10):1963â1974, 2014. 4
|
1504.03410#39
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 39,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "[2] A. Gionis, P. Indyk, and R. Motwani. Similarity search in In Proceedings of the Inter- high dimensions via hashing. national Conference on Very Large Data Bases, pages 518â 529, 1999. 5, 6\n[3] Y. Gong, S. Kumar, H. A. Rowley, and S. Lazebnik. Learning binary codes for high-dimensional data using bilinear projec- tions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 484â491, 2013. 1\nIterative quantization: A pro- In Proceed- crustean approach to learning binary codes. ings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 817â824, 2011. 1, 2, 5, 6\n[5] J. Ji, S. Yan, J. Li, G. Gao, Q. Tian, and B. Zhang. Batch- orthogonal locality-sensitive hashing for angular similarity. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 36(10):1963â1974, 2014. 4",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 40 |
[6] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Gir- shick, S. Guadarrama, and T. Darrell. Caffe: Convolu- tional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014. 6
[7] A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet clas- siï¬cation with deep convolutional neural networks. In Pro- ceedings of Advances in Neural Information Processing Sys- tems, pages 1106â1114, 2012. 2, 3, 6
[8] B. Kulis and T. Darrell. Learning to hash with binary re- constructive embeddings. In Proceedings of the Advances in Neural Information Processing Systems, pages 1042â1050, 2009. 1, 2, 5, 6
[9] B. Kulis and K. Grauman. Kernelized locality-sensitive In Proceedings of the hashing for scalable image search. IEEE International Conference on Computer Vision, pages 2130â2137, 2009. 1, 2
|
1504.03410#40
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 40,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "[6] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Gir- shick, S. Guadarrama, and T. Darrell. Caffe: Convolu- tional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014. 6\n[7] A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet clas- siï¬cation with deep convolutional neural networks. In Pro- ceedings of Advances in Neural Information Processing Sys- tems, pages 1106â1114, 2012. 2, 3, 6\n[8] B. Kulis and T. Darrell. Learning to hash with binary re- constructive embeddings. In Proceedings of the Advances in Neural Information Processing Systems, pages 1042â1050, 2009. 1, 2, 5, 6\n[9] B. Kulis and K. Grauman. Kernelized locality-sensitive In Proceedings of the hashing for scalable image search. IEEE International Conference on Computer Vision, pages 2130â2137, 2009. 1, 2",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 41 |
[10] X. Li, G. Lin, C. Shen, A. v. d. Hengel, and A. Dick. Learn- ing hash functions using column generation. In Proceedings of the International Conference on Machine Learning, pages 142â150, 2013. 3
[11] M. Lin, Q. Chen, and S. Yan. Network in network. In Pro- ceedings of the International Conference on Learning Rep- resentations, 2014. 2, 3
[12] W. Liu, J. Wang, R. Ji, Y.-G. Jiang, and S.-F. Chang. Super- vised hashing with kernels. In Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition, pages 2074â2081, 2012. 1, 2, 3, 5, 6
[13] W. Liu, J. Wang, S. Kumar, and S.-F. Chang. Hashing with graphs. In Proceedings of the International Conference on Machine Learning, pages 1â8, 2011. 2, 5
[14] X. Liu, J. He, B. Lang, and S.-F. Chang. Hash bit selection: a uniï¬ed solution for selection problems in hashing. In Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1570â1577, 2013. 1
|
1504.03410#41
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 41,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "[10] X. Li, G. Lin, C. Shen, A. v. d. Hengel, and A. Dick. Learn- ing hash functions using column generation. In Proceedings of the International Conference on Machine Learning, pages 142â150, 2013. 3\n[11] M. Lin, Q. Chen, and S. Yan. Network in network. In Pro- ceedings of the International Conference on Learning Rep- resentations, 2014. 2, 3\n[12] W. Liu, J. Wang, R. Ji, Y.-G. Jiang, and S.-F. Chang. Super- vised hashing with kernels. In Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition, pages 2074â2081, 2012. 1, 2, 3, 5, 6\n[13] W. Liu, J. Wang, S. Kumar, and S.-F. Chang. Hashing with graphs. In Proceedings of the International Conference on Machine Learning, pages 1â8, 2011. 2, 5\n[14] X. Liu, J. He, B. Lang, and S.-F. Chang. Hash bit selection: a uniï¬ed solution for selection problems in hashing. In Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1570â1577, 2013. 1",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 42 |
[15] B. Neyshabur, N. Srebro, R. Salakhutdinov, Y. Makarychev, and P. Yadollahpour. The power of asymmetry in binary hashing. In Advances in Neural Information Processing Sys- tems, pages 2823â2831, 2013. 4
[16] M. Norouzi and D. M. Blei. Minimal loss hashing for com- pact binary codes. In Proceedings of the International Con- ference on Machine Learning, pages 353â360, 2011. 1, 2, 5, 6
[17] M. Norouzi, D. J. Fleet, and R. Salakhutdinov. Hamming distance metric learning. In Proceedings of the Advances in Neural Information Processing Systems, pages 1â9, 2012. 2, 3
[18] A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145â175, 2001. 1, 2 [19] R. Salakhutdinov and G. Hinton. Learning a nonlinear em- bedding by preserving class neighbourhood structure. In Proceedings of the International Conference on Artiï¬cial In- telligence and Statistics, pages 412â419, 2007. 2
|
1504.03410#42
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 42,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "[15] B. Neyshabur, N. Srebro, R. Salakhutdinov, Y. Makarychev, and P. Yadollahpour. The power of asymmetry in binary hashing. In Advances in Neural Information Processing Sys- tems, pages 2823â2831, 2013. 4\n[16] M. Norouzi and D. M. Blei. Minimal loss hashing for com- pact binary codes. In Proceedings of the International Con- ference on Machine Learning, pages 353â360, 2011. 1, 2, 5, 6\n[17] M. Norouzi, D. J. Fleet, and R. Salakhutdinov. Hamming distance metric learning. In Proceedings of the Advances in Neural Information Processing Systems, pages 1â9, 2012. 2, 3\n[18] A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145â175, 2001. 1, 2 [19] R. Salakhutdinov and G. Hinton. Learning a nonlinear em- bedding by preserving class neighbourhood structure. In Proceedings of the International Conference on Artiï¬cial In- telligence and Statistics, pages 412â419, 2007. 2",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 43 |
[20] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229, 2013. 2
[21] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 2
[22] I. Sutskever, J. Martens, G. Dahl, and G. Hinton. On the importance of initialization and momentum in deep learning. In Proceedings of the 30th International Conference on Ma- chine Learning, pages 1139â1147, 2013. 6
[23] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabi- novich. Going deeper with convolutions. arXiv preprint arXiv:1409.4842, 2014. 2
|
1504.03410#43
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 43,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "[20] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229, 2013. 2\n[21] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 2\n[22] I. Sutskever, J. Martens, G. Dahl, and G. Hinton. On the importance of initialization and momentum in deep learning. In Proceedings of the 30th International Conference on Ma- chine Learning, pages 1139â1147, 2013. 6\n[23] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabi- novich. Going deeper with convolutions. arXiv preprint arXiv:1409.4842, 2014. 2",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.03410
| 44 |
[24] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face veriï¬ca- tion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1701â1708, 2014. 2
Semi-supervised IEEE Transactions on Pat- hashing for large-scale search. tern Analysis and Machine Intelligence, 34(12):2393â2406, 2012. 1
[26] Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In Proceedings of the Advances in Neural Information Process- ing Systems, pages 1753â1760, 2008. 2, 5, 6
[27] R. Xia, Y. Pan, H. Lai, C. Liu, and S. Yan. Supervised hashing for image retrieval via image representation learn- In Proceedings of the AAAI Conference on Artiï¬cial ing. Intellignece, pages 2156â2162, 2014. 1, 2, 3, 5, 6
|
1504.03410#44
|
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
|
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.
|
http://arxiv.org/pdf/1504.03410
|
Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan
|
cs.CV
|
This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 2015
| null |
cs.CV
|
20150414
|
20150414
|
[] |
{
"authors": "Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan",
"chunk_id": 44,
"doc_id": "1504.03410",
"primary_category": "cs.CV",
"published": 20150414,
"source": "http://arxiv.org/pdf/1504.03410",
"summary": "Similarity-preserving hashing is a widely-used method for nearest neighbour\nsearch in large-scale image retrieval tasks. For most existing hashing methods,\nan image is first encoded as a vector of hand-engineering visual features,\nfollowed by another separate projection or quantization step that generates\nbinary codes. However, such visual feature vectors may not be optimally\ncompatible with the coding process, thus producing sub-optimal hashing codes.\nIn this paper, we propose a deep architecture for supervised hashing, in which\nimages are mapped into binary codes via carefully designed deep neural\nnetworks. The pipeline of the proposed deep architecture consists of three\nbuilding blocks: 1) a sub-network with a stack of convolution layers to produce\nthe effective intermediate image features; 2) a divide-and-encode module to\ndivide the intermediate image features into multiple branches, each encoded\ninto one hash bit; and 3) a triplet ranking loss designed to characterize that\none image is more similar to the second image than to the third one. Extensive\nevaluations on several benchmark image datasets show that the proposed\nsimultaneous feature learning and hash coding pipeline brings substantial\nimprovements over other state-of-the-art supervised or unsupervised hashing\nmethods.",
"text": "[24] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face veriï¬ca- tion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1701â1708, 2014. 2\nSemi-supervised IEEE Transactions on Pat- hashing for large-scale search. tern Analysis and Machine Intelligence, 34(12):2393â2406, 2012. 1\n[26] Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In Proceedings of the Advances in Neural Information Process- ing Systems, pages 1753â1760, 2008. 2, 5, 6\n[27] R. Xia, Y. Pan, H. Lai, C. Liu, and S. Yan. Supervised hashing for image retrieval via image representation learn- In Proceedings of the AAAI Conference on Artiï¬cial ing. Intellignece, pages 2156â2162, 2014. 1, 2, 3, 5, 6",
"title": "Simultaneous Feature Learning and Hash Coding with Deep Neural Networks",
"year": 2015
}
|
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] |
1504.00702
| 0 |
6 1 0 2
r p A 9 1 ] G L . s c [
5 v 2 0 7 0 0 . 4 0 5 1 : v i X r a
Journal of Machine Learning Research 17 (2016) 1-40
Submitted 10/15; Published 4/16
# End-to-End Training of Deep Visuomotor Policies
Sergey Levineâ Chelsea Finnâ Trevor Darrell Pieter Abbeel Division of Computer Science University of California Berkeley, CA 94720-1776, USA â These authors contributed equally.
[email protected] [email protected] [email protected] [email protected]
Editor: Jan Peters
# Abstract
|
1504.00702#0
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 0,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "6 1 0 2\nr p A 9 1 ] G L . s c [\n5 v 2 0 7 0 0 . 4 0 5 1 : v i X r a\nJournal of Machine Learning Research 17 (2016) 1-40\nSubmitted 10/15; Published 4/16\n# End-to-End Training of Deep Visuomotor Policies\nSergey Levineâ Chelsea Finnâ Trevor Darrell Pieter Abbeel Division of Computer Science University of California Berkeley, CA 94720-1776, USA â These authors contributed equally.\[email protected] [email protected] [email protected] [email protected]\nEditor: Jan Peters\n# Abstract",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 1 |
Editor: Jan Peters
# Abstract
Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to- end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robotâs motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods. Keywords: Reinforcement Learning, Optimal Control, Vision, Neural Networks
# 1. Introduction
|
1504.00702#1
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 1,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "Editor: Jan Peters\n# Abstract\nPolicy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to- end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robotâs motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods. Keywords: Reinforcement Learning, Optimal Control, Vision, Neural Networks\n# 1. Introduction",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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1504.00702
| 2 |
# 1. Introduction
Robots can perform impressive tasks under human control, including surgery (Lanfranco et al., 2004) and household chores (Wyrobek et al., 2008). However, designing the perception and control software for autonomous operation remains a major challenge, even for basic tasks. Policy search methods hold the promise of allowing robots to automatically learn new behaviors through experience (Kober et al., 2010b; Deisenroth et al., 2011; Kalakrishnan et al., 2011; Deisenroth et al., 2013). However, policies learned using such methods often rely on a number of hand-engineered components for perception and control, so as to present the policy with a more manageable and low-dimensional representation of observations and actions. The vision system in particular can be complex and prone to errors, and it is typically not improved during policy training, nor adapted to the goal of the task.
In this article, we aim to answer the following question: can we acquire more eï¬ec- tive policies for sensorimotor control if the perception system is trained jointly with the control policy, rather than separately? In order to represent a policy that performs both
©2016 Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel.
Levine, Finn, Darrell, and Abbeel
hanger cube hammer bottle
hanger
cube
hammer
bottle
|
1504.00702#2
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 2,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "# 1. Introduction\nRobots can perform impressive tasks under human control, including surgery (Lanfranco et al., 2004) and household chores (Wyrobek et al., 2008). However, designing the perception and control software for autonomous operation remains a major challenge, even for basic tasks. Policy search methods hold the promise of allowing robots to automatically learn new behaviors through experience (Kober et al., 2010b; Deisenroth et al., 2011; Kalakrishnan et al., 2011; Deisenroth et al., 2013). However, policies learned using such methods often rely on a number of hand-engineered components for perception and control, so as to present the policy with a more manageable and low-dimensional representation of observations and actions. The vision system in particular can be complex and prone to errors, and it is typically not improved during policy training, nor adapted to the goal of the task.\nIn this article, we aim to answer the following question: can we acquire more eï¬ec- tive policies for sensorimotor control if the perception system is trained jointly with the control policy, rather than separately? In order to represent a policy that performs both\n©2016 Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel.\nLevine, Finn, Darrell, and Abbeel\nhanger cube hammer bottle \nhanger \ncube \nhammer \nbottle",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 3 |
Figure 1: Our method learns visuomotor policies that directly use camera image observa- tions (left) to set motor torques on a PR2 robot (right).
perception and control, we use deep neural networks. Deep neural network representations have recently seen widespread success in a variety of domains, such as computer vision and speech recognition, and even playing video games. However, using deep neural networks for real-world sensorimotor policies, such as robotic controllers that map image pixels and joint angles to motor torques, presents a number of unique challenges. Successful applications of deep neural networks typically rely on large amounts of data and direct supervision of the output, neither of which is available in robotic control. Real-world robot interaction data is scarce, and task completion is deï¬ned at a high level by means of a cost function, which means that the learning algorithm must determine on its own which action to take at each point. From the control perspective, a further complication is that observations from the robotâs sensors do not provide us with the full state of the system. Instead, important state information, such as the positions of task-relevant objects, must be inferred from inputs such as camera images.
|
1504.00702#3
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 3,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "Figure 1: Our method learns visuomotor policies that directly use camera image observa- tions (left) to set motor torques on a PR2 robot (right).\nperception and control, we use deep neural networks. Deep neural network representations have recently seen widespread success in a variety of domains, such as computer vision and speech recognition, and even playing video games. However, using deep neural networks for real-world sensorimotor policies, such as robotic controllers that map image pixels and joint angles to motor torques, presents a number of unique challenges. Successful applications of deep neural networks typically rely on large amounts of data and direct supervision of the output, neither of which is available in robotic control. Real-world robot interaction data is scarce, and task completion is deï¬ned at a high level by means of a cost function, which means that the learning algorithm must determine on its own which action to take at each point. From the control perspective, a further complication is that observations from the robotâs sensors do not provide us with the full state of the system. Instead, important state information, such as the positions of task-relevant objects, must be inferred from inputs such as camera images.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 4 |
We address these challenges by developing a guided policy search algorithm for senso- rimotor deep learning, as well as a novel CNN architecture designed for robotic control. Guided policy search converts policy search into supervised learning, by iteratively con- structing the training data using an eï¬cient model-free trajectory optimization procedure. We show that this can be formalized as an instance of Bregman ADMM (BADMM) (Wang and Banerjee, 2014), which can be used to show that the algorithm converges to a locally optimal solution. In our method, the full state of the system is observable at training time, but not at test time. For most tasks, providing the full state simply requires position- ing objects in one of several known positions for each trial during training. At test time, the learned CNN policy can handle novel, unknown conï¬gurations, and no longer requires full state information. Since the policy is optimized with supervised learning, we can use standard methods like stochastic gradient descent for training. Our CNNs have 92,000 pa- rameters and 7 layers, including a novel spatial feature point transformation that provides accurate spatial reasoning and reduces overï¬tting. This allows us to train
|
1504.00702#4
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 4,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "We address these challenges by developing a guided policy search algorithm for senso- rimotor deep learning, as well as a novel CNN architecture designed for robotic control. Guided policy search converts policy search into supervised learning, by iteratively con- structing the training data using an eï¬cient model-free trajectory optimization procedure. We show that this can be formalized as an instance of Bregman ADMM (BADMM) (Wang and Banerjee, 2014), which can be used to show that the algorithm converges to a locally optimal solution. In our method, the full state of the system is observable at training time, but not at test time. For most tasks, providing the full state simply requires position- ing objects in one of several known positions for each trial during training. At test time, the learned CNN policy can handle novel, unknown conï¬gurations, and no longer requires full state information. Since the policy is optimized with supervised learning, we can use standard methods like stochastic gradient descent for training. Our CNNs have 92,000 pa- rameters and 7 layers, including a novel spatial feature point transformation that provides accurate spatial reasoning and reduces overï¬tting. This allows us to train",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 5 |
and 7 layers, including a novel spatial feature point transformation that provides accurate spatial reasoning and reduces overï¬tting. This allows us to train our policies with relatively modest amounts of data and only tens of minutes of real-world interaction time. We evaluate our method by learning policies for inserting a block into a shape sorting cube, screwing a cap onto a bottle, ï¬tting the claw of a toy hammer under a nail with various grasps, and placing a coat hanger on a rack with a PR2 robot (see Figure 1). These tasks require localization, visual tracking, and handling complex contact dynamics. Our results demonstrate improvements in consistency and generalization from training visuomotor poli- cies end-to-end, when compared to training the vision and control components separately. We also present simulated comparisons that show that guided policy search outperforms a
|
1504.00702#5
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 5,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "and 7 layers, including a novel spatial feature point transformation that provides accurate spatial reasoning and reduces overï¬tting. This allows us to train our policies with relatively modest amounts of data and only tens of minutes of real-world interaction time. We evaluate our method by learning policies for inserting a block into a shape sorting cube, screwing a cap onto a bottle, ï¬tting the claw of a toy hammer under a nail with various grasps, and placing a coat hanger on a rack with a PR2 robot (see Figure 1). These tasks require localization, visual tracking, and handling complex contact dynamics. Our results demonstrate improvements in consistency and generalization from training visuomotor poli- cies end-to-end, when compared to training the vision and control components separately. We also present simulated comparisons that show that guided policy search outperforms a",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 7 |
# 2. Related Work
Reinforcement learning and policy search methods (Gullapalli, 1990; Williams, 1992) have been applied in robotics for playing games such as table tennis (Kober et al., 2010b), object manipulation (Gullapalli, 1995; Peters and Schaal, 2008; Kober et al., 2010a; Deisenroth et al., 2011; Kalakrishnan et al., 2011), locomotion (Benbrahim and Franklin, 1997; Kohl and Stone, 2004; Tedrake et al., 2004; Geng et al., 2006; Endo et al., 2008), and ï¬ight (Ng et al., 2004). Several recent papers provide surveys of policy search in robotics (Deisenroth et al., 2013; Kober et al., 2013). Such methods are typically applied to one component of the robot control pipeline, which often sits on top of a hand-designed controller, such as a PD controller, and accepts processed input, for example from an existing vision pipeline (Kalakrishnan et al., 2011). Our method learns policies that map visual input and joint encoder readings directly to the torques at the robotâs joints. By learning the entire map- ping from perception to control, the perception layers can be adapted to optimize task performance, and the motor control layers can be adapted to imperfect perception.
|
1504.00702#7
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 7,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "# 2. Related Work\nReinforcement learning and policy search methods (Gullapalli, 1990; Williams, 1992) have been applied in robotics for playing games such as table tennis (Kober et al., 2010b), object manipulation (Gullapalli, 1995; Peters and Schaal, 2008; Kober et al., 2010a; Deisenroth et al., 2011; Kalakrishnan et al., 2011), locomotion (Benbrahim and Franklin, 1997; Kohl and Stone, 2004; Tedrake et al., 2004; Geng et al., 2006; Endo et al., 2008), and ï¬ight (Ng et al., 2004). Several recent papers provide surveys of policy search in robotics (Deisenroth et al., 2013; Kober et al., 2013). Such methods are typically applied to one component of the robot control pipeline, which often sits on top of a hand-designed controller, such as a PD controller, and accepts processed input, for example from an existing vision pipeline (Kalakrishnan et al., 2011). Our method learns policies that map visual input and joint encoder readings directly to the torques at the robotâs joints. By learning the entire map- ping from perception to control, the perception layers can be adapted to optimize task performance, and the motor control layers can be adapted to imperfect perception.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 8 |
We represent our policies with convolutional neural networks (CNNs). CNNs have a long history in computer vision and deep learning (Fukushima, 1980; LeCun et al., 1989; Schmidhuber, 2015), and have recently gained prominence due to excellent results on a number of vision benchmarks (Ciresan et al., 2011; Krizhevsky et al., 2012; Ciresan et al., 2012; Girshick et al., 2014a; Tompson et al., 2014; LeCun et al., 2015; He et al., 2015). Most applications of CNNs focus on classiï¬cation, where locational information is discarded by means of successive pooling layers to provide for invariance (Lee et al., 2009). Applications to localization typically either use a sliding window (Girshick et al., 2014a) or object pro- posals (Endres and Hoiem, 2010; Uijlings et al., 2013; Girshick et al., 2014b) to localize the object, reducing the task to classiï¬cation, perform regression to a heatmap of manually labeled keypoints (Tompson et al., 2014), requiring precise knowledge of the object posi- tion in the image and camera calibration, or use 3D
|
1504.00702#8
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 8,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "We represent our policies with convolutional neural networks (CNNs). CNNs have a long history in computer vision and deep learning (Fukushima, 1980; LeCun et al., 1989; Schmidhuber, 2015), and have recently gained prominence due to excellent results on a number of vision benchmarks (Ciresan et al., 2011; Krizhevsky et al., 2012; Ciresan et al., 2012; Girshick et al., 2014a; Tompson et al., 2014; LeCun et al., 2015; He et al., 2015). Most applications of CNNs focus on classiï¬cation, where locational information is discarded by means of successive pooling layers to provide for invariance (Lee et al., 2009). Applications to localization typically either use a sliding window (Girshick et al., 2014a) or object pro- posals (Endres and Hoiem, 2010; Uijlings et al., 2013; Girshick et al., 2014b) to localize the object, reducing the task to classiï¬cation, perform regression to a heatmap of manually labeled keypoints (Tompson et al., 2014), requiring precise knowledge of the object posi- tion in the image and camera calibration, or use 3D",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 9 |
of manually labeled keypoints (Tompson et al., 2014), requiring precise knowledge of the object posi- tion in the image and camera calibration, or use 3D models to localize previously scanned objects (Pepik et al., 2012; Savarese and Fei-Fei, 2007). Many prior robotic applications of CNNs do not directly consider control, but employ CNNs for the perception component of a larger robotic system (Hadsell et al., 2009; Sung et al., 2015; Lenz et al., 2015b; Pinto and Gupta, 2015). We use a novel CNN architecture for our policies that automatically learn feature points that capture spatial information about the scene, without any supervision beyond the information from the robotâs encoders and camera.
|
1504.00702#9
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 9,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "of manually labeled keypoints (Tompson et al., 2014), requiring precise knowledge of the object posi- tion in the image and camera calibration, or use 3D models to localize previously scanned objects (Pepik et al., 2012; Savarese and Fei-Fei, 2007). Many prior robotic applications of CNNs do not directly consider control, but employ CNNs for the perception component of a larger robotic system (Hadsell et al., 2009; Sung et al., 2015; Lenz et al., 2015b; Pinto and Gupta, 2015). We use a novel CNN architecture for our policies that automatically learn feature points that capture spatial information about the scene, without any supervision beyond the information from the robotâs encoders and camera.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 10 |
Applications of deep learning in robotic control have been less prevalent in recent years than in visual recognition. Backpropagation through the dynamics and the image for- mation process is typically impractical, since they are often non-diï¬erentiable, and such long-range backpropagation can lead to extreme numerical instability, since the lineariza- tion of a suboptimal policy is likely to be unstable. This issue has also been observed in the related context of recurrent neural networks (Hochreiter et al., 2001; Pascanu and Bengio, 2012). The high dimensionality of the network also makes reinforcement learning diï¬cult (Deisenroth et al., 2013). Pioneering early work on neural network control used
3
Levine, Finn, Darrell, and Abbeel
|
1504.00702#10
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 10,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "Applications of deep learning in robotic control have been less prevalent in recent years than in visual recognition. Backpropagation through the dynamics and the image for- mation process is typically impractical, since they are often non-diï¬erentiable, and such long-range backpropagation can lead to extreme numerical instability, since the lineariza- tion of a suboptimal policy is likely to be unstable. This issue has also been observed in the related context of recurrent neural networks (Hochreiter et al., 2001; Pascanu and Bengio, 2012). The high dimensionality of the network also makes reinforcement learning diï¬cult (Deisenroth et al., 2013). Pioneering early work on neural network control used\n3\nLevine, Finn, Darrell, and Abbeel",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 11 |
small, simple networks (Pomerleau, 1989; Hunt et al., 1992; Bekey and Goldberg, 1992; Lewis et al., 1998; Bakker et al., 2003; Mayer et al., 2006), and has largely been supplanted by methods that use carefully designed policies that can be learned eï¬ciently with rein- forcement learning (Kober et al., 2013). More recent work on sensorimotor deep learning has tackled simple task-space motions (Lenz et al., 2015a; Lampe and Riedmiller, 2013) and used unsupervised learning to obtain low-dimensional state spaces from images (Lange et al., 2012). Such methods have been demonstrated on tasks with a low-dimensional un- derlying structure: Lenz et al. (2015a) controls the end-eï¬ector in 2D space, while Lange et al. (2012) controls a 2-dimensional slot car with 1-dimensional actions. Our experiments include full torque control of 7-DoF robotic arms interacting with objects, with 30-40 state dimensions. In simple synthetic environments, control from images has been addressed with image features (Jodogne and Piater, 2007), nonparametric methods (van Hoof et al., 2015),
|
1504.00702#11
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 11,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "small, simple networks (Pomerleau, 1989; Hunt et al., 1992; Bekey and Goldberg, 1992; Lewis et al., 1998; Bakker et al., 2003; Mayer et al., 2006), and has largely been supplanted by methods that use carefully designed policies that can be learned eï¬ciently with rein- forcement learning (Kober et al., 2013). More recent work on sensorimotor deep learning has tackled simple task-space motions (Lenz et al., 2015a; Lampe and Riedmiller, 2013) and used unsupervised learning to obtain low-dimensional state spaces from images (Lange et al., 2012). Such methods have been demonstrated on tasks with a low-dimensional un- derlying structure: Lenz et al. (2015a) controls the end-eï¬ector in 2D space, while Lange et al. (2012) controls a 2-dimensional slot car with 1-dimensional actions. Our experiments include full torque control of 7-DoF robotic arms interacting with objects, with 30-40 state dimensions. In simple synthetic environments, control from images has been addressed with image features (Jodogne and Piater, 2007), nonparametric methods (van Hoof et al., 2015),",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 12 |
environments, control from images has been addressed with image features (Jodogne and Piater, 2007), nonparametric methods (van Hoof et al., 2015), and unsupervised state-space learning (B¨ohmer et al., 2013; Jonschkowski and Brock, 2014). CNNs have also been trained to play video games with Q-learning, Monte Carlo tree search, and stochastic search (Mnih et al., 2013; Koutn´ık et al., 2013; Guo et al., 2014), and have been applied to simple simulated control tasks (Watter et al., 2015; Lillicrap et al., 2015). However, such methods have only been demonstrated on synthetic domains that lack the visual complexity of the real world, and require an impractical number of samples for real- world robotic learning. Our method is sample eï¬cient, requiring only minutes of interaction time. To the best of our knowledge, this is the ï¬rst method that can train deep visuomotor policies for complex, high-dimensional manipulation skills with direct torque control.
|
1504.00702#12
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 12,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "environments, control from images has been addressed with image features (Jodogne and Piater, 2007), nonparametric methods (van Hoof et al., 2015), and unsupervised state-space learning (B¨ohmer et al., 2013; Jonschkowski and Brock, 2014). CNNs have also been trained to play video games with Q-learning, Monte Carlo tree search, and stochastic search (Mnih et al., 2013; Koutn´ık et al., 2013; Guo et al., 2014), and have been applied to simple simulated control tasks (Watter et al., 2015; Lillicrap et al., 2015). However, such methods have only been demonstrated on synthetic domains that lack the visual complexity of the real world, and require an impractical number of samples for real- world robotic learning. Our method is sample eï¬cient, requiring only minutes of interaction time. To the best of our knowledge, this is the ï¬rst method that can train deep visuomotor policies for complex, high-dimensional manipulation skills with direct torque control.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 13 |
Learning visuomotor policies on a real robot requires handling complex observations and high dimensional policy representations. We tackle these challenges using guided pol- icy search. In guided policy search, the policy is optimized using supervised learning, which scales gracefully with the dimensionality of the policy. The training set for supervised learn- ing can be constructed using trajectory optimization under known dynamics (Levine and Koltun, 2013a,b, 2014; Mordatch and Todorov, 2014) and trajectory-centric reinforcement learning methods that operate under unknown dynamics (Levine and Abbeel, 2014; Levine et al., 2015), which is the approach taken in this work. In both cases, the supervision is adapted to the policy, to ensure that the ï¬nal policy can reproduce the training data. The use of supervised learning in the inner loop of iterative policy search has also been pro- posed in the context of imitation learning (Ross et al., 2011, 2013). However, such methods typically do not address the question of how the supervision should be adapted to the policy. The goal of our approach is also similar to visual servoing, which performs feedback control on feature points in a camera image (Espiau et al., 1992; Mohta et
|
1504.00702#13
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
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"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 13,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "Learning visuomotor policies on a real robot requires handling complex observations and high dimensional policy representations. We tackle these challenges using guided pol- icy search. In guided policy search, the policy is optimized using supervised learning, which scales gracefully with the dimensionality of the policy. The training set for supervised learn- ing can be constructed using trajectory optimization under known dynamics (Levine and Koltun, 2013a,b, 2014; Mordatch and Todorov, 2014) and trajectory-centric reinforcement learning methods that operate under unknown dynamics (Levine and Abbeel, 2014; Levine et al., 2015), which is the approach taken in this work. In both cases, the supervision is adapted to the policy, to ensure that the ï¬nal policy can reproduce the training data. The use of supervised learning in the inner loop of iterative policy search has also been pro- posed in the context of imitation learning (Ross et al., 2011, 2013). However, such methods typically do not address the question of how the supervision should be adapted to the policy. The goal of our approach is also similar to visual servoing, which performs feedback control on feature points in a camera image (Espiau et al., 1992; Mohta et",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 14 |
The goal of our approach is also similar to visual servoing, which performs feedback control on feature points in a camera image (Espiau et al., 1992; Mohta et al., 2014; Wilson et al., 1996). However, our visuomotor policies are entirely learned from real-world data, and do not require feature points or feedback controllers to be speciï¬ed by hand. This allows our method much more ï¬exibility in choosing how to use the visual signal. Our approach also does not require any sort of camera calibration, in contrast to many visual servoing methods (though not all â see e.g. J¨agersand et al. (1997); Yoshimi and Allen (1994)).
|
1504.00702#14
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 14,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "The goal of our approach is also similar to visual servoing, which performs feedback control on feature points in a camera image (Espiau et al., 1992; Mohta et al., 2014; Wilson et al., 1996). However, our visuomotor policies are entirely learned from real-world data, and do not require feature points or feedback controllers to be speciï¬ed by hand. This allows our method much more ï¬exibility in choosing how to use the visual signal. Our approach also does not require any sort of camera calibration, in contrast to many visual servoing methods (though not all â see e.g. J¨agersand et al. (1997); Yoshimi and Allen (1994)).",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 15 |
# 3. Background and Overview
In this section, we deï¬ne the visuomotor policy learning problem and present an overview of our approach. The core component of our approach is a guided policy search algorithm
4
End-to-End Training of Deep Visuomotor Policies
that separates the problem of learning visuomotor policies into separate supervised learning and trajectory learning phases, each of which is easier than optimizing the policy directly. We also discuss a policy architecture suitable for end-to-end learning of vision and control, and a training setup that allows our method to be applied to real robotic platforms.
# 3.1 Deï¬nitions and Problem Formulation
|
1504.00702#15
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 15,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "# 3. Background and Overview\nIn this section, we deï¬ne the visuomotor policy learning problem and present an overview of our approach. The core component of our approach is a guided policy search algorithm\n4\nEnd-to-End Training of Deep Visuomotor Policies\nthat separates the problem of learning visuomotor policies into separate supervised learning and trajectory learning phases, each of which is easier than optimizing the policy directly. We also discuss a policy architecture suitable for end-to-end learning of vision and control, and a training setup that allows our method to be applied to real robotic platforms.\n# 3.1 Deï¬nitions and Problem Formulation",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 16 |
In policy search, the goal is to learn a policy 79(u;|o,) that allows an agent to choose actions u, in response to observations o; to control a dynamical system, such as a robot. The policy comes from some parametric class parameterized by 9, which could be, for example, the weights of a neural network. The system is defined by states x,, actions u,, and observations o;. For example, x; might include the joint angles of the robot, the positions of objects in the world, and their time derivatives, u; might consist of motor torque commands, and o; might include an image from the robotâs onboard camera. In this paper, we address finite horizon episodic tasks with t ⬠[1,..., 7]. The states evolve in time according to the system dynamics p(x++1|x¢, uz), and the observations are, in general, a stochastic consequence of the states, according to p(o;|x;). Neither the dynamics nor the observation distribution are assumed to be known in general. For notational convenience, we will use 79(u;|x;) to denote the distribution over actions under the policy conditioned on the state. However, since the policy
|
1504.00702#16
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 16,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "In policy search, the goal is to learn a policy 79(u;|o,) that allows an agent to choose actions u, in response to observations o; to control a dynamical system, such as a robot. The policy comes from some parametric class parameterized by 9, which could be, for example, the weights of a neural network. The system is defined by states x,, actions u,, and observations o;. For example, x; might include the joint angles of the robot, the positions of objects in the world, and their time derivatives, u; might consist of motor torque commands, and o; might include an image from the robotâs onboard camera. In this paper, we address finite horizon episodic tasks with t ⬠[1,..., 7]. The states evolve in time according to the system dynamics p(x++1|x¢, uz), and the observations are, in general, a stochastic consequence of the states, according to p(o;|x;). Neither the dynamics nor the observation distribution are assumed to be known in general. For notational convenience, we will use 79(u;|x;) to denote the distribution over actions under the policy conditioned on the state. However, since the policy",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 19 |
# 3.2 Approach Summary
Our methods consists of two main components, which are illustrated in Figure 3. The first is a supervised learning algorithm that trains policies of the form 79(u;|o,) = N(u7 (oz), &(0z)), where both yâ¢(o¢) and (oz) are general nonlinear functions. In our implementation, 1 (0¢) is a deep convolutional neural network, while ©7(o;) is an observation-independent earned covariance, though other representations are possible. The second component is a rajectory-centric reinforcement learning (RL) algorithm that generates guiding distribu- ions p;(u;|x;) that provide the supervision used to train the policy. These two components orm a policy search algorithm that can be used to learn complex robotic tasks using only a high-level cost function ¢(x;, uz). During training, only samples from the guiding distribu- ions p;(u;|xz) are generated by running rollouts on the physical system, which avoids the need to execute partially trained neural network policies on physical hardware.
Supervised learning will not, in general, produce a policy with good long-horizon per- formance, since a small mistake on the part of the policy will place the system into states that are outside the distribution in the training data, causing compounding errors. To
5
|
1504.00702#19
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 19,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "# 3.2 Approach Summary\nOur methods consists of two main components, which are illustrated in Figure 3. The first is a supervised learning algorithm that trains policies of the form 79(u;|o,) = N(u7 (oz), &(0z)), where both yâ¢(o¢) and (oz) are general nonlinear functions. In our implementation, 1 (0¢) is a deep convolutional neural network, while ©7(o;) is an observation-independent earned covariance, though other representations are possible. The second component is a rajectory-centric reinforcement learning (RL) algorithm that generates guiding distribu- ions p;(u;|x;) that provide the supervision used to train the policy. These two components orm a policy search algorithm that can be used to learn complex robotic tasks using only a high-level cost function ¢(x;, uz). During training, only samples from the guiding distribu- ions p;(u;|xz) are generated by running rollouts on the physical system, which avoids the need to execute partially trained neural network policies on physical hardware.\nSupervised learning will not, in general, produce a policy with good long-horizon per- formance, since a small mistake on the part of the policy will place the system into states that are outside the distribution in the training data, causing compounding errors. To\n5",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 21 |
symbol definition example/details Markovian system state at time step t ⬠Joint angles, end-effector pose, object Posl- Xe 1,7] tions, and their velocities; dimensionality: ; 14 to 32 trol i t ti tep t ⬠[1,7] joint motor torque commands; dimensional- ur control or action at time step : ity: 7 (for the PR2 robot) RGB camera image, joint encoder readings oO observation at time step t ⬠[1, T] & velocities, end-effector pose; dimensional- ity: around 200,000 r trajectory: notational shorthand for a sequence of states T = {X1,U1,X2,U2,...,xr, ur} and actions , . . dista betwe a bject in th i L(Xt, Xe) cost function that defines the goal of the task istance Detween an object In be gripper and the target P(Xt+1|Xe, Ue) unknown system dynamics physics that govern the robot and any ob- jects it interacts with stochastic process that produces camera im- 01x vat istributi p(o|Xz) unknown observation distribution ages from system state learned nonlinear global policy parameter-|
|
1504.00702#21
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 21,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "symbol definition example/details Markovian system state at time step t ⬠Joint angles, end-effector pose, object Posl- Xe 1,7] tions, and their velocities; dimensionality: ; 14 to 32 trol i t ti tep t ⬠[1,7] joint motor torque commands; dimensional- ur control or action at time step : ity: 7 (for the PR2 robot) RGB camera image, joint encoder readings oO observation at time step t ⬠[1, T] & velocities, end-effector pose; dimensional- ity: around 200,000 r trajectory: notational shorthand for a sequence of states T = {X1,U1,X2,U2,...,xr, ur} and actions , . . dista betwe a bject in th i L(Xt, Xe) cost function that defines the goal of the task istance Detween an object In be gripper and the target P(Xt+1|Xe, Ue) unknown system dynamics physics that govern the robot and any ob- jects it interacts with stochastic process that produces camera im- 01x vat istributi p(o|Xz) unknown observation distribution ages from system state learned nonlinear global policy parameter-|",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 22 |
process that produces camera im- 01x vat istributi p(o|Xz) unknown observation distribution ages from system state learned nonlinear global policy parameter-| convolutional neural network, such as the To (ut|Oz) . . an : ized by weights 0 one in Figure 2 (ui lx) f (wilor)p(orlxe)a notational shorthand for observation-based 9 (ut |x. To (ur|Oz)p(Or|xz)do. . sys o\mee oe Or) Portlet aor policy conditioned on state (us[xe) learned local time-varying linear-Gaussian | time-varying linear-Gaussian controller has pituelx controller for initial state x} form N(Kux: + ki, Cri) (7) trajectory distribution for â7@(u,|xz):| notational shorthand for trajectory distribu- 0 P(x1) Wes To (We |Xt)P(Xt41[Xe, Us) tion induced by a policy
|
1504.00702#22
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 22,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "process that produces camera im- 01x vat istributi p(o|Xz) unknown observation distribution ages from system state learned nonlinear global policy parameter-| convolutional neural network, such as the To (ut|Oz) . . an : ized by weights 0 one in Figure 2 (ui lx) f (wilor)p(orlxe)a notational shorthand for observation-based 9 (ut |x. To (ur|Oz)p(Or|xz)do. . sys o\\mee oe Or) Portlet aor policy conditioned on state (us[xe) learned local time-varying linear-Gaussian | time-varying linear-Gaussian controller has pituelx controller for initial state x} form N(Kux: + ki, Cri) (7) trajectory distribution for â7@(u,|xz):| notational shorthand for trajectory distribu- 0 P(x1) Wes To (We |Xt)P(Xt41[Xe, Us) tion induced by a policy",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 23 |
Table 1: Summary of the notation frequently used in this article.
avoid this issue, the training data must come from the policyâs own state distribution (Ross et al., 2011). We achieve this by alternating between trajectory-centric RL and supervised learning. The RL stage adapts to the current policy Ïθ(ut|ot), providing supervision at states that are iteratively brought closer to the states visited by the policy. This is for- malized as a variant of the BADMM algorithm (Wang and Banerjee, 2014) for constrained optimization, which can be used to show that, at convergence, the policy Ïθ(ut|ot) and the guiding distributions pi(ut|xt) will exhibit the same behavior. This algorithm is derived in Section 4. The guiding distributions are substantially easier to optimize than learning the policy parameters directly (e.g., using model-free reinforcement learning), because they use the full state of the system xt, while the policy Ïθ(ut|ot) only uses the observations. This means that the method requires the full state to be known during training, but not at test time. This makes it possible to eï¬ciently learn complex visuomotor policies, but imposes additional assumptions on the observability of xt during training that we discuss in Section 4.
|
1504.00702#23
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 23,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "Table 1: Summary of the notation frequently used in this article.\navoid this issue, the training data must come from the policyâs own state distribution (Ross et al., 2011). We achieve this by alternating between trajectory-centric RL and supervised learning. The RL stage adapts to the current policy Ïθ(ut|ot), providing supervision at states that are iteratively brought closer to the states visited by the policy. This is for- malized as a variant of the BADMM algorithm (Wang and Banerjee, 2014) for constrained optimization, which can be used to show that, at convergence, the policy Ïθ(ut|ot) and the guiding distributions pi(ut|xt) will exhibit the same behavior. This algorithm is derived in Section 4. The guiding distributions are substantially easier to optimize than learning the policy parameters directly (e.g., using model-free reinforcement learning), because they use the full state of the system xt, while the policy Ïθ(ut|ot) only uses the observations. This means that the method requires the full state to be known during training, but not at test time. This makes it possible to eï¬ciently learn complex visuomotor policies, but imposes additional assumptions on the observability of xt during training that we discuss in Section 4.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 24 |
When learning visuomotor tasks, the policy Ïθ(ut|ot) is represented by a novel convo- lutional neural network (CNN) architecture, which we describe in Section 5.2. CNNs have enjoyed considerable success in computer vision (LeCun et al., 2015), but the most popular
6
# End-to-End Training of Deep Visuomotor Policies
RGB image convt conv conv3 spatial softmax feature motor . . . points torques 7x7 conv s fully fully fully stride 2 expected connected) connected >} connected J Ry ReLU [2D position ReLU. ReLU linear 240 109 a 40 40 7 109 109 robot configuration 39
Figure 2: Visuomotor policy architecture. The network contains three convolutional lay- ers, followed by a spatial softmax and an expected position layer that converts pixel-wise features to feature points, which are better suited for spatial computations. The points are concatenated with the robot conï¬guration, then passed through three fully connected layers to produce the torques.
|
1504.00702#24
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 24,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "When learning visuomotor tasks, the policy Ïθ(ut|ot) is represented by a novel convo- lutional neural network (CNN) architecture, which we describe in Section 5.2. CNNs have enjoyed considerable success in computer vision (LeCun et al., 2015), but the most popular\n6\n# End-to-End Training of Deep Visuomotor Policies\nRGB image convt conv conv3 spatial softmax feature motor . . . points torques 7x7 conv s fully fully fully stride 2 expected connected) connected >} connected J Ry ReLU [2D position ReLU. ReLU linear 240 109 a 40 40 7 109 109 robot configuration 39\nFigure 2: Visuomotor policy architecture. The network contains three convolutional lay- ers, followed by a spatial softmax and an expected position layer that converts pixel-wise features to feature points, which are better suited for spatial computations. The points are concatenated with the robot conï¬guration, then passed through three fully connected layers to produce the torques.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 25 |
architectures rely on large datasets and focus on semantic tasks such as classiï¬cation, often intentionally discarding spatial information. Our architecture, illustrated in Figure 2, uses a ï¬xed transformation from the last convolutional layer to a set of spatial feature points, which form a concise representation of the visual scene suitable for feedback control. Our network has 7 layers and around 92,000 parameters, which presents a major challenge for standard policy search methods (Deisenroth et al., 2013). To reduce the amount of experience needed to train visuomotor policies, we also introduce a pretraining scheme that allows us to train eï¬ective policies with a relatively small number of iterations. The pretraining steps are illustrated in Figure 3. The intuition behind our pretraining is that, although we ultimately seek to obtain sensorimotor policies that combine both vision and control, low-level aspects of vision can be initialized independently. To that end, we pretrain the convolu- tional layers of our network by predicting elements of xt that are not provided in the observation ot, such as the positions of objects in the scene. We also initially train the guiding trajectory distributions
|
1504.00702#25
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 25,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "architectures rely on large datasets and focus on semantic tasks such as classiï¬cation, often intentionally discarding spatial information. Our architecture, illustrated in Figure 2, uses a ï¬xed transformation from the last convolutional layer to a set of spatial feature points, which form a concise representation of the visual scene suitable for feedback control. Our network has 7 layers and around 92,000 parameters, which presents a major challenge for standard policy search methods (Deisenroth et al., 2013). To reduce the amount of experience needed to train visuomotor policies, we also introduce a pretraining scheme that allows us to train eï¬ective policies with a relatively small number of iterations. The pretraining steps are illustrated in Figure 3. The intuition behind our pretraining is that, although we ultimately seek to obtain sensorimotor policies that combine both vision and control, low-level aspects of vision can be initialized independently. To that end, we pretrain the convolu- tional layers of our network by predicting elements of xt that are not provided in the observation ot, such as the positions of objects in the scene. We also initially train the guiding trajectory distributions",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 26 |
elements of xt that are not provided in the observation ot, such as the positions of objects in the scene. We also initially train the guiding trajectory distributions pi(ut|xt) indepen- dently of the convolutional network until the trajecto- ries achieve a basic level of competence at the task, and then switch to full guided policy search with end-to-end In our implementation, we also training of Ïθ(ut|ot). initialize the ï¬rst layer ï¬lters from the model of Szegedy et al. (2014), which is trained on ImageNet (Deng et al., 2009) classiï¬cation. The initialization and pretraining scheme is described in Section 5.2.
|
1504.00702#26
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 26,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "elements of xt that are not provided in the observation ot, such as the positions of objects in the scene. We also initially train the guiding trajectory distributions pi(ut|xt) indepen- dently of the convolutional network until the trajecto- ries achieve a basic level of competence at the task, and then switch to full guided policy search with end-to-end In our implementation, we also training of Ïθ(ut|ot). initialize the ï¬rst layer ï¬lters from the model of Szegedy et al. (2014), which is trained on ImageNet (Deng et al., 2009) classiï¬cation. The initialization and pretraining scheme is described in Section 5.2.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 27 |
{Ï j i } pi Ïθ pi pi
# 4. Guided Policy Search with BADMM
Guided policy search transforms policy search into a supervised learning problem, where the training set is generated by a simple trajectory-centric RL algorithm. This algorithm
7
# Levine, Finn, Darrell, and Abbeel
optimizes linear-Gaussian controllers pi(ut|xt), and is described in Section 4.2. We refer to the trajectory distribution induced by pi(ut|xt) as pi(Ï ). Each pi(ut|xt) succeeds from diï¬erent initial states. For example, in the task of placing a cap on a bottle, these initial states correspond to diï¬erent positions of the bottle. By training on trajectories for multiple bottle positions, the ï¬nal CNN policy can succeed from all initial states, and can generalize to other states from the same distribution.
|
1504.00702#27
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 27,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "{Ï j i } pi Ïθ pi pi \n# 4. Guided Policy Search with BADMM\nGuided policy search transforms policy search into a supervised learning problem, where the training set is generated by a simple trajectory-centric RL algorithm. This algorithm\n7\n# Levine, Finn, Darrell, and Abbeel\noptimizes linear-Gaussian controllers pi(ut|xt), and is described in Section 4.2. We refer to the trajectory distribution induced by pi(ut|xt) as pi(Ï ). Each pi(ut|xt) succeeds from diï¬erent initial states. For example, in the task of placing a cap on a bottle, these initial states correspond to diï¬erent positions of the bottle. By training on trajectories for multiple bottle positions, the ï¬nal CNN policy can succeed from all initial states, and can generalize to other states from the same distribution.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 28 |
The ï¬nal policy Ïθ(ut|ot) learned with guided policy search is only provided with observations ot of the full state xt, and the dynamics are assumed to be unknown. A diagram of this method, which corresponds to an expanded version of the guided policy search box in Figure 3, is shown on the right. In the outer loop, we draw sample trajectories {Ï j i } for each ini- tial state on the physical system by running the corresponding controller pi(ut|xt). The samples are used to ï¬t the dynamics pi(xt+1|xt, ut) that are used to improve pi(ut|xt), and serve as training data for the policy. The inner loop alternates between optimizing each pi(Ï ) and optimizing the policy to match these trajectory distributions. The policy is trained to predict the actions along each trajectory from the observations ot, rather than the full state xt. This allows the policy to directly use raw observations at test time. This alternating optimization can be framed as an instance of the BADMM algorithm (Wang and Banerjee, 2014), which converges to a solution where the trajectory distributions and the policy have the same state distribution. This allows greedy supervised training of the policy to produce a policy with good long-horizon performance.
|
1504.00702#28
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 28,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "The ï¬nal policy Ïθ(ut|ot) learned with guided policy search is only provided with observations ot of the full state xt, and the dynamics are assumed to be unknown. A diagram of this method, which corresponds to an expanded version of the guided policy search box in Figure 3, is shown on the right. In the outer loop, we draw sample trajectories {Ï j i } for each ini- tial state on the physical system by running the corresponding controller pi(ut|xt). The samples are used to ï¬t the dynamics pi(xt+1|xt, ut) that are used to improve pi(ut|xt), and serve as training data for the policy. The inner loop alternates between optimizing each pi(Ï ) and optimizing the policy to match these trajectory distributions. The policy is trained to predict the actions along each trajectory from the observations ot, rather than the full state xt. This allows the policy to directly use raw observations at test time. This alternating optimization can be framed as an instance of the BADMM algorithm (Wang and Banerjee, 2014), which converges to a solution where the trajectory distributions and the policy have the same state distribution. This allows greedy supervised training of the policy to produce a policy with good long-horizon performance.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 29 |
outer loop run each pi(ut|xt) on robot inner loop samples {Ï j i } ï¬t dynamics optimize Ïθ w.r.t. Lθ optimize each pi(Ï ) w.r.t. Lp
# 4.1 Algorithm Derivation
Policy search methods minimize the expected cost Ex, [(7)], where 7 = {x1,u1,...,x7, ur} is a trajectory, and (7) = a 1 (Xt, uz) is the cost of an episode. In the fully observed case, the expectation is taken under 79(T) = p(x1) Tha mo (ur|Xt)p(Xt41|Xt, Uz). The final policy o(uz|oz) is conditioned on the observations o;, but 79(uz|xz) can be recovered as 79(uz|Xt) = J 79(u:|04)p(04|x;)do;. We will present the derivation in this section for 7(u;|x;), but we do not require knowledge of p(o;|x;) in the final algorithm. As discussed in Section 4.3, the integral will be evaluated with samples from the real system, which include both x; and o;. We begin by rewriting the expected cost minimization as a constrained problem:
|
1504.00702#29
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 29,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "outer loop run each pi(ut|xt) on robot inner loop samples {Ï j i } ï¬t dynamics optimize Ïθ w.r.t. Lθ optimize each pi(Ï ) w.r.t. Lp \n# 4.1 Algorithm Derivation\nPolicy search methods minimize the expected cost Ex, [(7)], where 7 = {x1,u1,...,x7, ur} is a trajectory, and (7) = a 1 (Xt, uz) is the cost of an episode. In the fully observed case, the expectation is taken under 79(T) = p(x1) Tha mo (ur|Xt)p(Xt41|Xt, Uz). The final policy o(uz|oz) is conditioned on the observations o;, but 79(uz|xz) can be recovered as 79(uz|Xt) = J 79(u:|04)p(04|x;)do;. We will present the derivation in this section for 7(u;|x;), but we do not require knowledge of p(o;|x;) in the final algorithm. As discussed in Section 4.3, the integral will be evaluated with samples from the real system, which include both x; and o;. We begin by rewriting the expected cost minimization as a constrained problem:",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 30 |
min Ep[¢(r)| s.t. p(u|xz) = 79(us|xt) V Xe, Ue, t, (1)
where we will refer to p(Ï ) as a guiding distribution. This formulation is equivalent to the original problem, since the constraint forces the two distributions to be identical. However, if we approximate the initial state distribution p(x1) with samples xi 1, we can choose p(Ï ) to be a class of distributions that is much easier to optimize than Ïθ, as we will show later. This will allow us to use simple local learning methods for p(Ï ), without needing to train the complex neural network policy Ïθ(ut|ot) directly with reinforcement learning, which would require a prohibitive amount of experience on real physical systems.
The constrained problem can be solved by a dual descent method, which alternates between minimizing the Lagrangian with respect to the primal variables, and incrementing
8
End-to-End Training of Deep Visuomotor Policies
|
1504.00702#30
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 30,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "min Ep[¢(r)| s.t. p(u|xz) = 79(us|xt) V Xe, Ue, t, (1)\nwhere we will refer to p(Ï ) as a guiding distribution. This formulation is equivalent to the original problem, since the constraint forces the two distributions to be identical. However, if we approximate the initial state distribution p(x1) with samples xi 1, we can choose p(Ï ) to be a class of distributions that is much easier to optimize than Ïθ, as we will show later. This will allow us to use simple local learning methods for p(Ï ), without needing to train the complex neural network policy Ïθ(ut|ot) directly with reinforcement learning, which would require a prohibitive amount of experience on real physical systems.\nThe constrained problem can be solved by a dual descent method, which alternates between minimizing the Lagrangian with respect to the primal variables, and incrementing\n8\nEnd-to-End Training of Deep Visuomotor Policies",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 31 |
8
End-to-End Training of Deep Visuomotor Policies
the Lagrange multipliers by their subgradient. Minimization of the Lagrangian with respect to p(Ï ) and θ is done in alternating fashion: minimizing with respect to θ corresponds to supervised learning (making Ïθ match p(Ï )), and minimizing with respect to p(Ï ) consists of one or more trajectory optimization problems. The dual descent method we use is based on BADMM (Wang and Banerjee, 2014), a variant of ADMM (Boyd et al., 2011) that augments the Lagrangian with a Bregman divergence between the constrained variables. We use the KL-divergence as the Bregman constraint, which is particularly convenient for working with probability distributions. We will also modify the constraint p(ut|xt) = Ïθ(ut|xt) by multiplying both sides by p(xt), to get p(ut|xt)p(xt) = Ïθ(ut|xt)p(xt). This constraint is equivalent, but has the convenient property that we can express the Lagrangian in terms of expectations. The BADMM augmented Lagrangians for θ and p are therefore given by
|
1504.00702#31
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 31,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "8\nEnd-to-End Training of Deep Visuomotor Policies\nthe Lagrange multipliers by their subgradient. Minimization of the Lagrangian with respect to p(Ï ) and θ is done in alternating fashion: minimizing with respect to θ corresponds to supervised learning (making Ïθ match p(Ï )), and minimizing with respect to p(Ï ) consists of one or more trajectory optimization problems. The dual descent method we use is based on BADMM (Wang and Banerjee, 2014), a variant of ADMM (Boyd et al., 2011) that augments the Lagrangian with a Bregman divergence between the constrained variables. We use the KL-divergence as the Bregman constraint, which is particularly convenient for working with probability distributions. We will also modify the constraint p(ut|xt) = Ïθ(ut|xt) by multiplying both sides by p(xt), to get p(ut|xt)p(xt) = Ïθ(ut|xt)p(xt). This constraint is equivalent, but has the convenient property that we can express the Lagrangian in terms of expectations. The BADMM augmented Lagrangians for θ and p are therefore given by",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 32 |
T Lo(O,p) = > Ep (xy ay) EX u,)] + Ey(xe)mo (uslxe) [Ax:url _ Ep(xeu,) Ax:uel +r 464 (8, p) t=1 T Ly(p, 6) = > Evycxeu) (EX, uz)] Tr Eoy(xe) mo (uelxe) Axe.ue] _ Ey(xeur) xen T a (9, P), t=1
where λxt,ut is the Lagrange multiplier for state xt and action ut at time t, and Ïθ Ïp t (θ, p) are expectations of the KL-divergences:
(0, p) are
Ot (p,9) = Encx,)[Dxx (p(uelxe)|| 79 (uelxe))] 9 (8, p) = Eycx,)(Dxu(mo (uy) [lp (ul x2)]Dual descent with alternating primal minimization is then described by the following steps:
|
1504.00702#32
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 32,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "T Lo(O,p) = > Ep (xy ay) EX u,)] + Ey(xe)mo (uslxe) [Ax:url _ Ep(xeu,) Ax:uel +r 464 (8, p) t=1 T Ly(p, 6) = > Evycxeu) (EX, uz)] Tr Eoy(xe) mo (uelxe) Axe.ue] _ Ey(xeur) xen T a (9, P), t=1\nwhere λxt,ut is the Lagrange multiplier for state xt and action ut at time t, and Ïθ Ïp t (θ, p) are expectations of the KL-divergences:\n(0, p) are\nOt (p,9) = Encx,)[Dxx (p(uelxe)|| 79 (uelxe))] 9 (8, p) = Eycx,)(Dxu(mo (uy) [lp (ul x2)]Dual descent with alternating primal minimization is then described by the following steps:",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 33 |
T 6 < arg min)? Eoy(xce)mro (uelxe) Axe] + 10% (0, p) t=1 T pear min)? Enyce au) E(C%t; Wt) â Aree ay] + 4d? (p, t=1 Axes â Areas + 04 (779 (Ue Xt) (Xt) â p(Ur|Xt)P(%t))# t (p, θ)
This procedure is an instance of BADMM, and therefore inherits its convergence guarantees. Note that we drop terms that are independent of the optimization variables on each line. The parameter α is a step size. As with most augmented Lagrangian methods, the weight νt is set heuristically, as described in Appendix A.1.
|
1504.00702#33
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 33,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "T 6 < arg min)? Eoy(xce)mro (uelxe) Axe] + 10% (0, p) t=1 T pear min)? Enyce au) E(C%t; Wt) â Aree ay] + 4d? (p, t=1 Axes â Areas + 04 (779 (Ue Xt) (Xt) â p(Ur|Xt)P(%t))# t (p, θ)\nThis procedure is an instance of BADMM, and therefore inherits its convergence guarantees. Note that we drop terms that are independent of the optimization variables on each line. The parameter α is a step size. As with most augmented Lagrangian methods, the weight νt is set heuristically, as described in Appendix A.1.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 34 |
The dynamics only aï¬ect the optimization with respect to p(Ï ). In order to make this optimization eï¬cient, we choose p(Ï ) to be a mixture of N Gaussians pi(Ï ), one for each initial state sample xi 1. This makes the action conditionals pi(ut|xt) and the dynamics pi(xt+1|xt, ut) linear-Gaussian, as discussed in Section 4.2. This is a reasonable choice when the system is deterministic, or the noise is Gaussian or small, and we found that this approach is suï¬ciently tolerant to noise for use on real physical systems. Our choice of p also assumes that the policy Ïθ(ut|ot) is conditionally Gaussian. This is also reasonable, since the mean and covariance of Ïθ(ut|ot) can be any nonlinear function of the observations
9
# Levine, Finn, Darrell, and Abbeel
ot, which themselves are a function of the unobserved state xt. In Section 4.2, we show how these assumptions enable each pi(Ï ) to be optimized very eï¬ciently. We will refer to pi(Ï ) as guiding distributions, since they serve to focus the policy on good, low-cost behaviors.
|
1504.00702#34
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 34,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "The dynamics only aï¬ect the optimization with respect to p(Ï ). In order to make this optimization eï¬cient, we choose p(Ï ) to be a mixture of N Gaussians pi(Ï ), one for each initial state sample xi 1. This makes the action conditionals pi(ut|xt) and the dynamics pi(xt+1|xt, ut) linear-Gaussian, as discussed in Section 4.2. This is a reasonable choice when the system is deterministic, or the noise is Gaussian or small, and we found that this approach is suï¬ciently tolerant to noise for use on real physical systems. Our choice of p also assumes that the policy Ïθ(ut|ot) is conditionally Gaussian. This is also reasonable, since the mean and covariance of Ïθ(ut|ot) can be any nonlinear function of the observations\n9\n# Levine, Finn, Darrell, and Abbeel\not, which themselves are a function of the unobserved state xt. In Section 4.2, we show how these assumptions enable each pi(Ï ) to be optimized very eï¬ciently. We will refer to pi(Ï ) as guiding distributions, since they serve to focus the policy on good, low-cost behaviors.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 35 |
Aside from learning pi(Ï ), we must choose a tractable way to represent the inï¬nite set of constraints p(ut|xt)p(xt) = Ïθ(ut|xt)p(xt). One approximate approach proposed in prior work is to replace the exact constraints with expectations of features (Peters et al., 2010). When the features consist of linear, quadratic, or higher order monomial functions of the random variable, this can be viewed as a constraint on the moments of the distributions. If we only use the ï¬rst moment, we get a constraint on the expected action: Ep(ut|xt)p(xt)[ut] = EÏθ(ut|xt)p(xt)[ut]. If the stochasticity in the dynamics is low, as we assumed previously, the optimal solution for each pi(Ï ) will have low entropy, making this ï¬rst moment constraint a reasonable approximation. The KL-divergence terms in the augmented Lagrangians will still serve to softly enforce agreement between the higher moments. While this simpliï¬cation is quite drastic, we found that it was more stable in practice than including higher moments, likely because these higher moments are harder to estimate accurately with a limited number of samples. The alternating optimization is now given by
|
1504.00702#35
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 35,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "Aside from learning pi(Ï ), we must choose a tractable way to represent the inï¬nite set of constraints p(ut|xt)p(xt) = Ïθ(ut|xt)p(xt). One approximate approach proposed in prior work is to replace the exact constraints with expectations of features (Peters et al., 2010). When the features consist of linear, quadratic, or higher order monomial functions of the random variable, this can be viewed as a constraint on the moments of the distributions. If we only use the ï¬rst moment, we get a constraint on the expected action: Ep(ut|xt)p(xt)[ut] = EÏθ(ut|xt)p(xt)[ut]. If the stochasticity in the dynamics is low, as we assumed previously, the optimal solution for each pi(Ï ) will have low entropy, making this ï¬rst moment constraint a reasonable approximation. The KL-divergence terms in the augmented Lagrangians will still serve to softly enforce agreement between the higher moments. While this simpliï¬cation is quite drastic, we found that it was more stable in practice than including higher moments, likely because these higher moments are harder to estimate accurately with a limited number of samples. The alternating optimization is now given by",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 36 |
T 0<¢ arg min Epcxz)mo(uelxe) [uf Apel + 1.69 (0, p) (2) t=1
# t=1 T
pe arg min)? Eny(x.uz)(E(Xt Ws) â Uf Ae] + MeO (D, 9) (3) t=1
λµt â λµt + ανt(EÏθ(ut|xt)p(xt)[ut] â Ep(ut|xt)p(xt)[ut]),
where λµt is the Lagrange multiplier on the expected action at time t. In the rest of the paper, we will use Lθ(θ, p) and Lp(p, θ) to denote the two augmented Lagrangians in Equations (2) and (3), respectively. In the next two sections, we will describe how Lp(p, θ) can be optimized with respect to p under unknown dynamics, and how Lθ(θ, p) can be optimized for complex, high-dimensional policies. Implementation details of the BADMM optimization are presented in Appendix A.1.
# 4.2 Trajectory Optimization under Unknown Dynamics
|
1504.00702#36
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 36,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "T 0<¢ arg min Epcxz)mo(uelxe) [uf Apel + 1.69 (0, p) (2) t=1\n# t=1 T\npe arg min)? Eny(x.uz)(E(Xt Ws) â Uf Ae] + MeO (D, 9) (3) t=1\nλµt â λµt + ανt(EÏθ(ut|xt)p(xt)[ut] â Ep(ut|xt)p(xt)[ut]),\nwhere λµt is the Lagrange multiplier on the expected action at time t. In the rest of the paper, we will use Lθ(θ, p) and Lp(p, θ) to denote the two augmented Lagrangians in Equations (2) and (3), respectively. In the next two sections, we will describe how Lp(p, θ) can be optimized with respect to p under unknown dynamics, and how Lθ(θ, p) can be optimized for complex, high-dimensional policies. Implementation details of the BADMM optimization are presented in Appendix A.1.\n# 4.2 Trajectory Optimization under Unknown Dynamics",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 37 |
# 4.2 Trajectory Optimization under Unknown Dynamics
Since the Lagrangian £,(p, 0) in the previous section factorizes over the mixture elements in p(t) = 30; pi(r), we describe the trajectory optimization method for a single Gaussian p(t). When there are multiple mixture elements, this procedure is applied in parallel to each pi(T). Since p(T) is Gaussian, the conditionals p(x;+41|x,, uz) and p(u;,|x;), which correspond to the dynamics and the controller, are time-varying linear-Gaussian, and given by
p(ut|xt) = N (Ktxt + kt, Ct) p(xt+1|xt, ut) = N (fxtxt + futut + fct, Ft).
This type of controller can be learned eï¬ciently with a small number of real-world samples, making it a good choice for optimizing the guiding distributions. Since a diï¬erent set of time- varying linear-Gaussian dynamics is ï¬tted for each initial state, this dynamics representation can model any continuous deterministic system that can be locally linearized. Stochastic dynamics can violate the local linearity assumption in principle, but we found that in practice this representation was well suited for a wide variety of noisy real-world tasks.
10
|
1504.00702#37
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 37,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "# 4.2 Trajectory Optimization under Unknown Dynamics\nSince the Lagrangian £,(p, 0) in the previous section factorizes over the mixture elements in p(t) = 30; pi(r), we describe the trajectory optimization method for a single Gaussian p(t). When there are multiple mixture elements, this procedure is applied in parallel to each pi(T). Since p(T) is Gaussian, the conditionals p(x;+41|x,, uz) and p(u;,|x;), which correspond to the dynamics and the controller, are time-varying linear-Gaussian, and given by\np(ut|xt) = N (Ktxt + kt, Ct) p(xt+1|xt, ut) = N (fxtxt + futut + fct, Ft).\nThis type of controller can be learned eï¬ciently with a small number of real-world samples, making it a good choice for optimizing the guiding distributions. Since a diï¬erent set of time- varying linear-Gaussian dynamics is ï¬tted for each initial state, this dynamics representation can model any continuous deterministic system that can be locally linearized. Stochastic dynamics can violate the local linearity assumption in principle, but we found that in practice this representation was well suited for a wide variety of noisy real-world tasks.\n10",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 38 |
10
End-to-End Training of Deep Visuomotor Policies
The dynamics are determined by the environment. If they are known, p(u;|x;) can be optimized with a variant of the iterative linear-quadratic-Gaussian regulator (iLQG) (Li and Todorov, 2004; Levine and Koltun, 2013a), which is a variant of DDP (Jacobson and Mayne, 1970). In the case of unknown dynamics, we can fit p(x:41|Xz, Uz) to sample trajectories sampled from the trajectory distribution at the previous iteration, denoted f(r). If p(7) is too different from p(T), these samples will not give a good estimate of p(x:+1|xz, uz), and the optimization will diverge. To avoid this, we can bound the change from p(7) to p(T) in terms of their KL-divergence by a step size â¬, producing the following constrained problem:
i Ly(p,9) s.t. Dxr(y p Se. te 3 p(p, 4) s KL(P(T)||B(7)) < â¬
|
1504.00702#38
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 38,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "10\nEnd-to-End Training of Deep Visuomotor Policies\nThe dynamics are determined by the environment. If they are known, p(u;|x;) can be optimized with a variant of the iterative linear-quadratic-Gaussian regulator (iLQG) (Li and Todorov, 2004; Levine and Koltun, 2013a), which is a variant of DDP (Jacobson and Mayne, 1970). In the case of unknown dynamics, we can fit p(x:41|Xz, Uz) to sample trajectories sampled from the trajectory distribution at the previous iteration, denoted f(r). If p(7) is too different from p(T), these samples will not give a good estimate of p(x:+1|xz, uz), and the optimization will diverge. To avoid this, we can bound the change from p(7) to p(T) in terms of their KL-divergence by a step size â¬, producing the following constrained problem:\ni Ly(p,9) s.t. Dxr(y p Se. te 3 p(p, 4) s KL(P(T)||B(7)) < â¬",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 39 |
This type of policy update has previously been proposed by several authors in the con- text of policy search (Bagnell and Schneider, 2003; Peters and Schaal, 2008; Peters et al., 2010; Levine and Abbeel, 2014). In the case when p(Ï ) is Gaussian, this problem can be solved eï¬ciently using dual gradient descent, while the dynamics p(xt+1|xt, ut) are ï¬tted to samples gathered by running the previous controller Ëp(ut|xt) on the robot. Fitting a global Gaussian mixture model to tuples (xt, ut, xt+1) and using it as a prior for ï¬tting the dynamics p(xt+1|xt, ut) serves to greatly reduce the sample complexity. We describe the dynamics ï¬tting procedure in detail in Appendix A.3.
|
1504.00702#39
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 39,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "This type of policy update has previously been proposed by several authors in the con- text of policy search (Bagnell and Schneider, 2003; Peters and Schaal, 2008; Peters et al., 2010; Levine and Abbeel, 2014). In the case when p(Ï ) is Gaussian, this problem can be solved eï¬ciently using dual gradient descent, while the dynamics p(xt+1|xt, ut) are ï¬tted to samples gathered by running the previous controller Ëp(ut|xt) on the robot. Fitting a global Gaussian mixture model to tuples (xt, ut, xt+1) and using it as a prior for ï¬tting the dynamics p(xt+1|xt, ut) serves to greatly reduce the sample complexity. We describe the dynamics ï¬tting procedure in detail in Appendix A.3.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 40 |
Note that the trajectory optimization cost function £,(p,0) also depends on the policy mo(uz|xz), while we only have access to 79(u;|oz). In order to compute a local quadratic expansion of the KL-divergence term Dkr (p(uz|x¢)||7(uz|xz)) inside L,(p, 0) for iLQG, we also estimate a linearization of the mean of the conditionally Gaussian policy 7(u;|o;) with respect to the state x;, using the same procedure that we use to linearize the dynamics. The data for this estimation consists of tuples {x}, E, a(u,|oi)[Ui]}, which we can obtain because both the states x} and the observations 0} are available for all of the samples evaluated on the real physical system.
|
1504.00702#40
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 40,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "Note that the trajectory optimization cost function £,(p,0) also depends on the policy mo(uz|xz), while we only have access to 79(u;|oz). In order to compute a local quadratic expansion of the KL-divergence term Dkr (p(uz|x¢)||7(uz|xz)) inside L,(p, 0) for iLQG, we also estimate a linearization of the mean of the conditionally Gaussian policy 7(u;|o;) with respect to the state x;, using the same procedure that we use to linearize the dynamics. The data for this estimation consists of tuples {x}, E, a(u,|oi)[Ui]}, which we can obtain because both the states x} and the observations 0} are available for all of the samples evaluated on the real physical system.",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 41 |
This constrained optimization is performed in the âinner loopâ of the optimization described in the previous section, and the KL-divergence constraint Dx i(p(7)||B(7)) < ⬠imposes a step size on the trajectory update. The overall algorithm then becomes an instance of generalized BADMM (Wang and Banerjee, 2014). Note that the augmented Lagrangian £L,(p, @) consists of an expectation under p(7) of a quantity that is independent of p. We can locally approximate this quantity with a quadratic by using a quadratic expansion of (xz, uz), and fitting a linear-Gaussian to 79(u;|x;) with the same method we used for the dynamics. We can then solve the primal optimization in the dual gradient descent procedure with a standard LQR backward pass. This is significantly simpler and much faster than the forward-backward dynamic programming procedure employed in previous work (Levine and Abbeel, 2014; Levine and Koltun, 2014). This improvement is enabled by the use of BADMM, which allows us to always formulate the KL-divergence term in the
|
1504.00702#41
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 41,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "This constrained optimization is performed in the âinner loopâ of the optimization described in the previous section, and the KL-divergence constraint Dx i(p(7)||B(7)) < ⬠imposes a step size on the trajectory update. The overall algorithm then becomes an instance of generalized BADMM (Wang and Banerjee, 2014). Note that the augmented Lagrangian £L,(p, @) consists of an expectation under p(7) of a quantity that is independent of p. We can locally approximate this quantity with a quadratic by using a quadratic expansion of (xz, uz), and fitting a linear-Gaussian to 79(u;|x;) with the same method we used for the dynamics. We can then solve the primal optimization in the dual gradient descent procedure with a standard LQR backward pass. This is significantly simpler and much faster than the forward-backward dynamic programming procedure employed in previous work (Levine and Abbeel, 2014; Levine and Koltun, 2014). This improvement is enabled by the use of BADMM, which allows us to always formulate the KL-divergence term in the",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 42 |
Levine and Koltun, 2014). This improvement is enabled by the use of BADMM, which allows us to always formulate the KL-divergence term in the Lagrangian with the distribution being optimized as the first argument. Since the KL-divergence is convex in its first argument, this makes the corresponding optimization significantly easier. The details of this LQR-based dual gradient descent algorithm are derived in Appendix A.4. We can further improve the efficiency of the method by allowing samples from multiple trajectories p;(7) to be used to fit a shared dynamics p(x:41|Xz, Uz), while the controllers pi(uz|x¢) are allowed to vary. This makes sense when the initial states of these trajectories
|
1504.00702#42
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 42,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "Levine and Koltun, 2014). This improvement is enabled by the use of BADMM, which allows us to always formulate the KL-divergence term in the Lagrangian with the distribution being optimized as the first argument. Since the KL-divergence is convex in its first argument, this makes the corresponding optimization significantly easier. The details of this LQR-based dual gradient descent algorithm are derived in Appendix A.4. We can further improve the efficiency of the method by allowing samples from multiple trajectories p;(7) to be used to fit a shared dynamics p(x:41|Xz, Uz), while the controllers pi(uz|x¢) are allowed to vary. This makes sense when the initial states of these trajectories",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 43 |
11
Levine, Finn, Darrell, and Abbeel
are similar, and they therefore visit similar regions. This allows us to draw just a single sample from each pi(Ï ) at each iteration, allowing us to handle many more initial states.
# 4.3 Supervised Policy Optimization
Since the policy parameters θ participate only in the constraints of the optimization problem in Equation (1), optimizing the policy corresponds to minimizing the KL-divergence between the policy and trajectory distribution, as well as the expectation of λT µtut. For a conditional Gaussian policy of the form Ïθ(ut|ot) = N (µÏ(ot), ΣÏ(ot)), the objective is
N T 1 _ £66.) =ay Yd Eriixnor) [tt[Cz;'=" (0r)] log |=" (0r)| i=1t=1 +(H" (01) â Hp (x1) Cai" (U⢠(Ot) â Mei (Xe)) + BAeâ (04)]
|
1504.00702#43
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 43,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "11\nLevine, Finn, Darrell, and Abbeel\nare similar, and they therefore visit similar regions. This allows us to draw just a single sample from each pi(Ï ) at each iteration, allowing us to handle many more initial states.\n# 4.3 Supervised Policy Optimization\nSince the policy parameters θ participate only in the constraints of the optimization problem in Equation (1), optimizing the policy corresponds to minimizing the KL-divergence between the policy and trajectory distribution, as well as the expectation of λT µtut. For a conditional Gaussian policy of the form Ïθ(ut|ot) = N (µÏ(ot), ΣÏ(ot)), the objective is\nN T 1 _ £66.) =ay Yd Eriixnor) [tt[Cz;'=\" (0r)] log |=\" (0r)| i=1t=1 +(H\" (01) â Hp (x1) Cai\" (U⢠(Ot) â Mei (Xe)) + BAeâ (04)]",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 44 |
where µp ti(xt) is the mean of pi(ut|xt) and Cti is the covariance, and the expectation is eval- uated using samples from each pi(Ï ) with corresponding observations ot. The observations are sampled from p(ot|xt) by recording camera images on the real system. Since the input to µÏ(ot) and ΣÏ(ot) is not the state xt, but only an observation ot, we can train the policy to directly use raw observations. Note that Lθ(θ, p) is simply a weighted quadratic loss on the diï¬erence between the policy mean and the mean action of the trajectory distribution, oï¬set by the Lagrange multiplier. The weighting is the precision matrix of the conditional in the trajectory distribution, which is equal to the curvature of its cost-to-go function (Levine and Koltun, 2013a). This has an intuitive interpretation: Lθ(θ, p) penalizes de- viation from the trajectory distribution, with a penalty that is locally proportional to its cost-to-go. At convergence, when the policy Ïθ(ut|ot) takes the same actions as pi(ut|xt), their Q-functions are equal, and the supervised policy objective becomes equivalent to the policy iteration objective (Levine and Koltun, 2014)
|
1504.00702#44
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 44,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "where µp ti(xt) is the mean of pi(ut|xt) and Cti is the covariance, and the expectation is eval- uated using samples from each pi(Ï ) with corresponding observations ot. The observations are sampled from p(ot|xt) by recording camera images on the real system. Since the input to µÏ(ot) and ΣÏ(ot) is not the state xt, but only an observation ot, we can train the policy to directly use raw observations. Note that Lθ(θ, p) is simply a weighted quadratic loss on the diï¬erence between the policy mean and the mean action of the trajectory distribution, oï¬set by the Lagrange multiplier. The weighting is the precision matrix of the conditional in the trajectory distribution, which is equal to the curvature of its cost-to-go function (Levine and Koltun, 2013a). This has an intuitive interpretation: Lθ(θ, p) penalizes de- viation from the trajectory distribution, with a penalty that is locally proportional to its cost-to-go. At convergence, when the policy Ïθ(ut|ot) takes the same actions as pi(ut|xt), their Q-functions are equal, and the supervised policy objective becomes equivalent to the policy iteration objective (Levine and Koltun, 2014)",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
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] |
1504.00702
| 45 |
In this work, we optimize Lθ(θ, p) with respect to θ using stochastic gradient descent (SGD), a standard method for neural network training. The covariance of the Gaussian policy does not depend on the observation in our implementation, though adding this de- pendence would be straightforward. Since training complex neural networks requires a substantial number of samples, we found it beneï¬cial to include sampled observations from previous iterations into the policy optimization, evaluating the action µp ti(xt) at their corre- sponding states using the current trajectory distributions. Since these samples come from the wrong state distribution, we use importance sampling and weight them according to the ratio of their probability under the current distribution p(xt) and the one they were sampled from, which is straightforward to evaluate under the estimated linear-Gaussian dynamics (Levine and Koltun, 2013b).
# 4.4 Comparison with Prior Guided Policy Search Methods
We presented a guided policy search method where the policy is trained on observations, while the trajectories are trained on the full state. The BADMM formulation of guided policy search is new to this work, though several prior guided policy search methods based on constrained optimization have been proposed. Levine and Koltun (2014) proposed a formulation similar to Equation (1), but with a constraint on the KL-divergence between
12
# End-to-End Training of Deep Visuomotor Policies
|
1504.00702#45
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 45,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "In this work, we optimize Lθ(θ, p) with respect to θ using stochastic gradient descent (SGD), a standard method for neural network training. The covariance of the Gaussian policy does not depend on the observation in our implementation, though adding this de- pendence would be straightforward. Since training complex neural networks requires a substantial number of samples, we found it beneï¬cial to include sampled observations from previous iterations into the policy optimization, evaluating the action µp ti(xt) at their corre- sponding states using the current trajectory distributions. Since these samples come from the wrong state distribution, we use importance sampling and weight them according to the ratio of their probability under the current distribution p(xt) and the one they were sampled from, which is straightforward to evaluate under the estimated linear-Gaussian dynamics (Levine and Koltun, 2013b).\n# 4.4 Comparison with Prior Guided Policy Search Methods\nWe presented a guided policy search method where the policy is trained on observations, while the trajectories are trained on the full state. The BADMM formulation of guided policy search is new to this work, though several prior guided policy search methods based on constrained optimization have been proposed. Levine and Koltun (2014) proposed a formulation similar to Equation (1), but with a constraint on the KL-divergence between\n12\n# End-to-End Training of Deep Visuomotor Policies",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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] |
1504.00702
| 46 |
12
# End-to-End Training of Deep Visuomotor Policies
p(Ï ) and Ïθ. This results in a more complex, non-convex forward-backward trajectory optimization phase. Since the BADMM formulation solves a convex problem during the trajectory optimization phase, it is substantially faster and easier to implement and use, especially when the number of trajectories pi(Ï ) is large.
The use of ADMM for guided policy search was also proposed by Mordatch and Todorov (2014) for deterministic policies under known dynamics. This approach requires known, de- terministic dynamics and trains deterministic policies. Furthermore, because this approach uses a simple quadratic augmented Lagrangian term, it further requires penalty terms on the gradient of the policy to account for local feedback. Our approach enforces this feed- back behavior due to the higher moments included in the KL-divergence term, but does not require computing the second derivative of the policy.
# 5. End-to-End Visuomotor Policies
|
1504.00702#46
|
End-to-End Training of Deep Visuomotor Policies
|
Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception and control systems jointly end-to-end provide better
performance than training each component separately? To this end, we develop a
method that can be used to learn policies that map raw image observations
directly to torques at the robot's motors. The policies are represented by deep
convolutional neural networks (CNNs) with 92,000 parameters, and are trained
using a partially observed guided policy search method, which transforms policy
search into supervised learning, with supervision provided by a simple
trajectory-centric reinforcement learning method. We evaluate our method on a
range of real-world manipulation tasks that require close coordination between
vision and control, such as screwing a cap onto a bottle, and present simulated
comparisons to a range of prior policy search methods.
|
http://arxiv.org/pdf/1504.00702
|
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel
|
cs.LG, cs.CV, cs.RO
|
updating with revisions for JMLR final version
| null |
cs.LG
|
20150402
|
20160419
|
[
{
"id": "1509.06113"
},
{
"id": "1509.02971"
},
{
"id": "1512.03385"
}
] |
{
"authors": "Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel",
"chunk_id": 46,
"doc_id": "1504.00702",
"primary_category": "cs.LG",
"published": 20150402,
"source": "http://arxiv.org/pdf/1504.00702",
"summary": "Policy search methods can allow robots to learn control policies for a wide\nrange of tasks, but practical applications of policy search often require\nhand-engineered components for perception, state estimation, and low-level\ncontrol. In this paper, we aim to answer the following question: does training\nthe perception and control systems jointly end-to-end provide better\nperformance than training each component separately? To this end, we develop a\nmethod that can be used to learn policies that map raw image observations\ndirectly to torques at the robot's motors. The policies are represented by deep\nconvolutional neural networks (CNNs) with 92,000 parameters, and are trained\nusing a partially observed guided policy search method, which transforms policy\nsearch into supervised learning, with supervision provided by a simple\ntrajectory-centric reinforcement learning method. We evaluate our method on a\nrange of real-world manipulation tasks that require close coordination between\nvision and control, such as screwing a cap onto a bottle, and present simulated\ncomparisons to a range of prior policy search methods.",
"text": "12\n# End-to-End Training of Deep Visuomotor Policies\np(Ï ) and Ïθ. This results in a more complex, non-convex forward-backward trajectory optimization phase. Since the BADMM formulation solves a convex problem during the trajectory optimization phase, it is substantially faster and easier to implement and use, especially when the number of trajectories pi(Ï ) is large.\nThe use of ADMM for guided policy search was also proposed by Mordatch and Todorov (2014) for deterministic policies under known dynamics. This approach requires known, de- terministic dynamics and trains deterministic policies. Furthermore, because this approach uses a simple quadratic augmented Lagrangian term, it further requires penalty terms on the gradient of the policy to account for local feedback. Our approach enforces this feed- back behavior due to the higher moments included in the KL-divergence term, but does not require computing the second derivative of the policy.\n# 5. End-to-End Visuomotor Policies",
"title": "End-to-End Training of Deep Visuomotor Policies",
"year": 2015
}
|
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