A weakly-supervised learning framework named as complementary-label has been proposed recently, where each sample is equipped with a single complementary label that denotes one of the classes does not belong to. However, existing methods cannot learn from easily accessible unlabeled samples and multiple labels, which are more informative. In this paper, to remove these limitations, we propose n...