Maximizing Incremental Information Entropy for Contrastive Learning

• URL: http://arxiv.org/abs/2603.12594v1
• Date: Fri, 13 Mar 2026 02:51:37 GMT
• Title: Maximizing Incremental Information Entropy for Contrastive Learning
• Abstract summary: We propose IE-CL (Incremental-Entropy Contrastive Learning), a framework that explicitly optimize the entropy gain between augmented views.<n>Our theoretical framework reframes the challenge by identifying an information encoder and proposes a joint optimization of two components.<n>Experiments on CIFAR-10/100, STL-10, and ImageNet demonstrate that IE-CL consistently improves performance under small-batch settings.
• Score: 42.45787033288968
• License: http://creativecommons.org/licenses/by/4.0/
• 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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