text: 137,87,1337385549271879681,1296555996303761408,Zemel Group,Check out our new paper Flexible Few-Shot Learning -- the same object can belong to different classes depending on context. We found unsupervised representation is better than supervised. A short version at NeurIPS metalearn workshop today at 10 EST. <LINK> <LINK> Joint work by @mengyer @Eleni30fillou @kcjacksonwang @james_r_lucas Jake Snell @xaqlab @AToliasLab and Rich Zemel,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,2,"['Check out our new paper Flexible Few-Shot Learning -- the same object can belong to different classes depending on context. We found unsupervised representation is better than supervised. A short version at NeurIPS metalearn workshop today at 10 EST. <LINK> <LINK>', 'Joint work by @mengyer @Eleni30fillou @kcjacksonwang @james_r_lucas Jake Snell @xaqlab @AToliasLab and Rich Zemel']",20,12,378