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Learning to classify new categories based on just one or a few examples is along-standing challenge in modern computer vision. In this work, we proposes asimple yet effective method for few-shot (and one-shot) object recognition. Ourapproach is based on a modified auto-encoder, denoted Delta-encoder, thatlearns to synthesize new samples for an unseen category just by seeing fewexamples from it. The synthesized samples are then used to train a classifier.The proposed approach learns to both extract transferable intra-classdeformations, or "deltas", between same-class pairs of training examples, andto apply those deltas to the few provided examples of a novel class (unseenduring training) in order to efficiently synthesize samples from that newclass. The proposed method improves over the state-of-the-art in one-shotobject-recognition and compares favorably in the few-shot case. Upon acceptancecode will be made available.

Delta-encoder: an effective sample synthesis method for few-shot object recognition