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InfoResearchPeer-reviewed
Robust Trusted Conflictive Multiview Collaborative Contrastive Learning
- Published
- Record updated
Summary
The paper proposes RCMCL, a Robust Trusted Conflictive Multiview Collaborative Contrastive Learning method, to address multiview learning models that ignore decision uncertainty and degrade on conflictive instances caused by misaligned multiview data. RCMCL combines evidential deep neural network views with dissonance-based evidence contrastive learning and collaborative learning of consistent and complementary evidence. The authors report that experiments on eight benchmark datasets show RCMCL outperforms state-of-the-art methods.