Maximizing Incremental Information Entropy for Contrastive Learning
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. Our theoretical framework reframes the challenge by identifying the encoder as an information bottleneck and proposes a joint optimization of two components: a learnable transformation for entropy generation and an encoder regularizer for its p...
| Authors | Jiansong Zhang, Zhuoqin Yang, Xu Wu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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