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Positive Pair Geometry Matters: Optimal Transport for Contrastive Learning of Visual Representations

arXiv · AI, language, vision and robotics · article · Sep 21, 2026 · UTC

Contrastive self-supervised learning has achieved strong performance by learning representations from multiple augmented views of the same image. However, most existing methods construct positive pairs using independently sampled stochastic augmentations, which may alter semantic content and ignore the intrinsic geometry of the data distribution. In this work, we propose OTCLR, an optimal transport-aware framework for contrastive learning representations that generates geometry-consistent positive samples. Instead of directly contrasting two randomly augmented views, we construct intermediate

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