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Study Ties Augmentation Graph Structure to Contrastive Learning Approximability

A theoretical paper examines the foundations of contrastive learning, a method that uses data augmentation to learn feature representations without large labeled datasets. The authors analyze how the structure of the augmentation graph relates to whether neural networks can approximate the resulting objective. The work aims to fill gaps in the theoretical understanding of why contrastive learning works in practice.

contrastive-learning/research/theory/self-supervised