text: 293,127,1141503748323176454,887992016,Luke Metz,"Our work exploring the use of learned optimizers to make more robust image models is on arXiv! We find that in some cases learned optimizers are capable of learning more robustness image classifiers! <LINK> <LINK> This was a fun ICML workshop paper using some of the technology we developed in (<LINK>) for new applications! Thanks to my awesome collaborators: @niru_m, Jonathon Shlens, @jaschasd, @ekindogus",https://arxiv.org/abs/1906.03367,"State-of-the art vision models can achieve superhuman performance on image classification tasks when testing and training data come from the same distribution. However, when models are tested on corrupted images (e.g. due to scale changes, translations, or shifts in brightness or contrast), performance degrades significantly. Here, we explore the possibility of meta-training a learned optimizer that can train image classification models such that they are robust to common image corruptions. Specifically, we are interested training models that are more robust to noise distributions not present in the training data. We find that a learned optimizer meta-trained to produce models which are robust to Gaussian noise trains models that are more robust to Gaussian noise at other scales compared to traditional optimizers like Adam. The effect of meta-training is more complicated when targeting a more general set of noise distributions, but led to improved performance on half of held-out corruption tasks. Our results suggest that meta-learning provides a novel approach for studying and improving the robustness of deep learning models. ",Using learned optimizers to make models robust to input noise,2,"['Our work exploring the use of learned optimizers to make more robust image models is on arXiv! We find that in some cases learned optimizers are capable of learning more robustness image classifiers!\n\n<LINK> <LINK>', 'This was a fun ICML workshop paper using some of the technology we developed in (https://t.co/gIiqPsbmQx) for new applications!\nThanks to my awesome collaborators: @niru_m, Jonathon Shlens, @jaschasd, @ekindogus']",19,06,408