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

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