arXiv Machine Learning September 11, 2026 By Jaehyoung Jeon, Cheolsu Lim, Myungjoo Kang

Divergence-Based Similarity Function for Multi-View Contrastive Learning

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The paper introduces a divergence-based similarity function (DSF) for multi-view contrastive learning, representing each set of augmented views as a distribution and measuring similarity via distribution divergence. DSF captures joint structure across all views, outperforming prior pairwise methods on tasks such as kNN classification, linear evaluation, transfer learning, and distribution shift. It also offers greater efficiency and eliminates the need for a temperature hyperparameter, unlike cosine similarity.

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