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Learning to Model the Tail
2017
Neural Information Processing Systems
We describe an approach to learning from long-tailed, imbalanced datasets that are prevalent in real-world settings. Here, the challenge is to learn accurate "fewshot" models for classes in the tail of the class distribution, for which little data is available. We cast this problem as transfer learning, where knowledge from the data-rich classes in the head of the distribution is transferred to the data-poor classes in the tail. Our key insights are as follows. First, we propose to transfer
dblp:conf/nips/WangRH17
fatcat:62h7orilzzektez4cmxuftztay