Semi-supervised Multi-label Learning with Balanced Binary Angular Margin Loss Ximing Li

The current prevalent ideas are estimating pseudo-labels of unlabeled samples with SSL techniques and inducing MLL classifiers with both labeled and pseudo-labeled samples in a self-training manner.