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Prototypical networks\nlearn a metric space in which classification can be performed by computing\ndistances to prototype representations of each class. Compared to recent\napproaches for few-shot learning, they reflect a simpler inductive bias that is\nbeneficial in this limited-data regime, and achieve excellent results. We\nprovide an analysis showing that some simple design decisions can yield\nsubstantial improvements over recent approaches involving complicated\narchitectural choices and meta-learning. We further extend prototypical\nnetworks to zero-shot learning and achieve state-of-the-art results on the\nCU-Birds dataset.","url_abs":"http://arxiv.org/abs/1703.05175v2","url_pdf":"http://arxiv.org/pdf/1703.05175v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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