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Low-shot visual learning---the ability to recognize novel object categoriesfrom very few examples---is a hallmark of human visual intelligence. Existingmachine learning approaches fail to generalize in the same way. To makeprogress on this foundational problem, we present a low-shot learning benchmarkon complex images that mimics challenges faced by recognition systems in thewild. We then propose a) representation regularization techniques, and b)techniques to hallucinate additional training examples for data-starvedclasses. Together, our methods improve the effectiveness of convolutionalnetworks in low-shot learning, improving the one-shot accuracy on novel classesby 2.3x on the challenging ImageNet dataset.

Low-shot Visual Recognition by Shrinking and Hallucinating Features