Hard Sample Aware Robust Contrastive Learning for Multi-View Clustering

Multi-view clustering aims to divide samples into several clusters, by mining and utilizing the consistency and complementarity of multi-view data. Recent years, numerous deep contrastive multi-view clustering methods have been proposed to address the false negative issue by using self-supervised information. However, the quality of these self-supervised information was rarely taken into consideration, and using these information without discrimination can compromise training, leading to sub optimal performance. To tackle this issue, we propose Hard Sample Aware Robust Contrastive Learning for Multi-View Clustering(HearMVC). Concretely, we use self-supervised information and similarity to determine hard samples. The model focuses on these hard samples by assigning higher weights to enhance discriminative capability. Moreover, we utilize the confidence of self-supervised cluster assignment as weights, to strengthen the learning to confident samples and weaken the influence of unconfident samples. By simultaneously considering the weighting of hardness and confidence, our method can achieve best robustness and strongest discriminative capability. Extensive experiments on public datesets verify the effectiveness of our method.