Hao-Zhe Liu

Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning

Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional pseudo-labeling methods encounter difficulties when dealing with instances associated with multiple labels and an unknow…