text: 405,88,1337378119821488128,980524179739963392,James  Lucas,"1/6 Check our new paper on Flexible Few-Shot Learning! <LINK> We extend FSL to include flexible classification criterion in each episode. Unsupervised representation learning beats supervised approaches. Short version @ #NeurIPS2020 metalearn workshop, 10am EST <LINK> 2/6 In the FFSL setting, each episode includes a support set, query set, AND a hidden context. The context determines how each example is classified and varies across episodes. At test time, we see novel contexts that weren’t included in the training set. <LINK> 3/6 We build two new benchmark datasets based on Celeb-A and Zappos-50k, where the context is determined by a choice of object attributes. Existing episodic training methods perform poorly on these tasks, but unsupervised representation learning gets closer to oracle performance <LINK> 4/6 Why do existing methods fail? We analyze training a protonet in a toy FFSL problem and identify that, unlike in the FSL setting, the protonet is encouraged to destroy information relevant for test-time contexts. <LINK> 5/6 The unsupervised representation learning approaches (U/UFT) learn localized features relevant to the test-time context. This allows them to generalize well. <LINK> 6/6 Many additional results included in our paper:  Including: careful evaluation of our proposed method, further Celeb-A + Zappos-50k experiments, evaluation on an ImageNet FFSL task, + much more! <LINK>",https://arxiv.org/abs/2012.05895,"Semantic concepts are frequently defined by combinations of underlying attributes. As mappings from attributes to classes are often simple, attribute-based representations facilitate novel concept learning with zero or few examples. A significant limitation of existing attribute-based learning paradigms, such as zero-shot learning, is that the attributes are assumed to be known and fixed. In this work we study the rapid learning of attributes that were not previously labeled. Compared to standard few-shot learning of semantic classes, in which novel classes may be defined by attributes that were relevant at training time, learning new attributes imposes a stiffer challenge. We found that supervised learning with training attributes does not generalize well to new test attributes, whereas self-supervised pre-training brings significant improvement. We further experimented with random splits of the attribute space and found that predictability of test attributes provides an informative estimate of a model's generalization ability. ",Few-Shot Attribute Learning,6,"['1/6 Check our new paper on Flexible Few-Shot Learning! <LINK>\n\nWe extend FSL to include flexible classification criterion in each episode. Unsupervised representation learning beats supervised approaches.\n\nShort version @ #NeurIPS2020 metalearn workshop, 10am EST <LINK>', '2/6 In the FFSL setting, each episode includes a support set, query set, AND a hidden context.\n\nThe context determines how each example is classified and varies across episodes.\n\nAt test time, we see novel contexts that weren’t included in the training set. https://t.co/fRqRs2vmGd', '3/6 We build two new benchmark datasets based on Celeb-A and Zappos-50k, where the context is determined by a choice of object attributes.\n\nExisting episodic training methods perform poorly on these tasks, but unsupervised representation learning gets closer to oracle performance https://t.co/z0Vrt9abNy', '4/6 Why do existing methods fail?\n\nWe analyze training a protonet in a toy FFSL problem and identify that, unlike in the FSL setting, the protonet is encouraged to destroy information relevant for test-time contexts. https://t.co/7GAiqlxvuB', '5/6 The unsupervised representation learning approaches (U/UFT) learn localized features relevant to the test-time context. This allows them to generalize well. https://t.co/zLNYuLhpjw', '6/6 Many additional results included in our paper: \n\nIncluding: careful evaluation of our proposed method, further Celeb-A + Zappos-50k experiments, evaluation on an ImageNet FFSL task, + much more! https://t.co/4zAhXIBFQp']",20,12,1414