RelationMatch: Matching In-batch Relationships for Semi-supervised Learning
This addresses the challenge of leveraging unlabeled data in SSL for machine learning practitioners, offering a novel method that is not incremental.
The paper tackled the problem of semi-supervised learning by proposing RelationMatch, a framework that enforces in-batch relational consistency, resulting in a 15.21% accuracy improvement over FlexMatch on STL-10.
Semi-supervised learning has emerged as a pivotal approach for leveraging scarce labeled data alongside abundant unlabeled data. Despite significant progress, prevailing SSL methods predominantly enforce consistency between different augmented views of individual samples, thereby overlooking the rich relational structure inherent within a mini-batch. In this paper, we present RelationMatch, a novel SSL framework that explicitly enforces in-batch relational consistency through a Matrix Cross-Entropy (MCE) loss function. The proposed MCE loss is rigorously derived from both matrix analysis and information geometry perspectives, ensuring theoretical soundness and practical efficacy. Extensive empirical evaluations on standard benchmarks, including a notable 15.21% accuracy improvement over FlexMatch on STL-10, demonstrate that RelationMatch not only advances state-of-the-art performance but also provides a principled foundation for incorporating relational cues in SSL.