Learning compositional representations for few-shot recognition
Deep learning representations lack the compositionality property, which is instrumental for the human ability to learn novel concepts from a few examples. In this work we investigate several approaches to enforcing this property during training. The resulting models demonstrate significant improvements in the few-shot setting.