AI RESEARCH
Maximizing Incremental Information Entropy for Contrastive Learning
arXiv CS.LG
•
ArXi:2603.12594v1 Announce Type: new Contrastive learning has achieved remarkable success in self-supervised representation learning, often guided by information-theoretic objectives such as mutual information maximization. Motivated by the limitations of static augmentations and rigid invariance constraints, we propose IE-CL (Incremental-Entropy Contrastive Learning), a framework that explicitly optimizes the entropy gain between augmented views while preserving semantic consistency.