2024
Semi-supervised Multi-label Learning with Balanced Binary Angular Margin Loss
NeurIPS 2024spotlight
Semi-supervised multi-label learning (SSMLL) refers to inducing classifiers using a small number of samples with multiple labels and many unlabeled samples. The prevalent solution of SSMLL involves forming pseudo-labels for unlabeled samples and inducing classifiers using both labeled and pseudo-lab…