Self-supervised knowledge distillation for complementary label learning

In this paper, we tackle a new learning paradigm called learning from complementary labels, where the training data specifies classes that instances do not belong to, instead of the accuracy labels. In general, it is more efficient to collect the complementary labels compared with collecting the supervised ones, with no need for selecting the correct one from a number of candidates. While current state-of-the-art methods design various loss functions to train competitive models by the limited