Learning from Complementary Labels

NeurIPS 2017 Takashi IshidaGang NiuWeihua HuMasashi Sugiyama

Collecting labeled data is costly and thus a critical bottleneck in real-world classification tasks. To mitigate this problem, we propose a novel setting, namely learning from complementary labels for multi-class classification... (read more)

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