Maximizing Incremental Information Entropy for Contrastive Learning

2026-03-12

AI Summary

This work addresses the limited information utilization in contrastive learning caused by static data augmentations and rigid invariance constraints. To this end, we propose the Information Entropy-optimized Contrastive Learning (IE-CL) framework, which explicitly maximizes the incremental information entropy gain between augmented views—a novel formulation in contrastive representation learning. IE-CL models the encoder as an information bottleneck and jointly optimizes learnable data transformations, an information entropy maximization mechanism, and an encoder regularizer to enhance representation quality while preserving semantic consistency. The IE-CL module is designed to be seamlessly integrated into existing contrastive methods and demonstrates consistent performance improvements across CIFAR-10/100, STL-10, and ImageNet under small-batch settings, validating its effectiveness and generalizability.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Information Theory
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• contrastive learning
• information entropy
• data augmentation
• representation learning
• information bottleneck

Innovation

• incremental entropy
• contrastive learning
• information bottleneck
• learnable augmentation
• self-supervised representation learning