Internal Discrepancy for Contrastive Learning: Generalization Bounds and Downstream Behaviors

The paper introduces the concept of internal discrepancy, measuring the gap between the mean representation of positive samples and latent cluster centers in contrastive learning. It derives a generalization bound for downstream supervised tasks that depends on this discrepancy, providing a unifying framework for existing assumptions and motivating a relaxed k‑means loss to reduce it. The study also explores how contrastive learning affects downstream behavior, particularly in class‑imbalanced settings, and establishes properties of the resulting supervised losses.

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