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arXiv · 2603.12594

Maximizing Incremental Information Entropy for Contrastive Learning

Jiansong Zhang·Zhuoqin Yang·Xu Wu·Xiaoling Luo·Peizhong Liu·Linlin Shen
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Abstract

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 preservation. Experiments on CIFAR-10/100, STL-10, and ImageNet demonstrate that IE-CL consistently improves performance under small-batch settings. Moreover, our core modules can be seamlessly integrated into existing frameworks. This work bridges theoretical principles and practice, offering a new perspective in contrastive learning.

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Jiansong Zhang, Zhuoqin Yang, Xu Wu, Xiaoling Luo, Peizhong Liu, Linlin Shen. 2026-03-13. Maximizing Incremental Information Entropy for Contrastive Learning. https://arxiv.org/abs/2603.12594

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