Robust Trusted Conflictive Multiview Collaborative Contrastive Learning

Although multiview learning methods have been widely studied, they mostly focus on improving accuracy while ignoring decision uncertainty. In the real world, multiview data often encounters misalignment issues, resulting in conflictive instances and further limiting the application of these methods in safety-critical domains. Recently, some efforts have been made to improve the reliability of multiview learning methods by estimating decision uncertainty, but most methods often experience performance degradation due to their inability to handle conflictive instances. To address this issue, we propose a Robust Trusted Conflictive Multiview Collaborative Contrastive Learning (RCMCL) method, which enhances the model's robustness and generalization ability in conflictive multiview scenarios. Specifically, RCMCL first uses an evidential deep neural network to construct view-specific opinions, and then employs dissonance-based evidence contrastive learning to enhance the consistency of these opinions across different views. Subsequently, RCMCL performs collaborative learning of consistent evidence and complementary evidence. It first introduces the vacuity degree into the complementary evidence to extract more useful information, and then employs category-level contrastive learning to separate consistent and complementary evidence. In addition, consistent and complementary evidence is combined to make a joint decision. Finally, experimental results on eight benchmark datasets verify the superiority of RCMCL over state-of-the-art methods.