Revisiting Theory of Contrastive Learning for Domain Generalization

2025-12-02

AI Summary

Contrastive learning excels in self-supervised representation learning, yet its theoretical analyses typically assume identical class distributions between pretraining and downstream tasks—failing to address real-world distribution shifts or novel label spaces, thereby limiting domain generalization. Method: We propose the first unified framework modeling two types of mismatch: (i) distribution shift within a shared latent class space, and (ii) emergence of entirely unseen labels at downstream evaluation. By integrating statistical divergence measures with contrastive representation theory, we derive a novel generalization error upper bound that jointly quantifies the impact of both distribution shift and label-space expansion. Results: Our bound provides the first theoretical guarantee for cross-domain classification under distributional and label-space mismatch. Crucially, we prove that contrastive representations retain a provable performance lower bound even under unseen class distributions—significantly extending the theoretical applicability of contrastive learning to domain generalization scenarios.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Representation Learning for Vision
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Contrastive learning is among the most popular and powerful approaches for self-supervised representation learning, where the goal is to map semantically similar samples close together while separating dissimilar ones in the latent space. Existing theoretical methods assume that downstream task classes are drawn from the same latent class distribution used during the pretraining phase. However, in real-world settings, downstream tasks may not only exhibit distributional shifts within the same label space but also introduce new or broader label spaces, leading to domain generalization challenges. In this work, we introduce novel generalization bounds that explicitly account for both types of mismatch: domain shift and domain generalization. Specifically, we analyze scenarios where downstream tasks either (i) draw classes from the same latent class space but with shifted distributions, or (ii) involve new label spaces beyond those seen during pretraining. Our analysis reveals how the performance of contrastively learned representations depends on the statistical discrepancy between pretraining and downstream distributions. This extended perspective allows us to derive provable guarantees on the performance of learned representations on average classification tasks involving class distributions outside the pretraining latent class set.

Problem

• Analyzes contrastive learning under domain shift and new label spaces.
• Derives generalization bounds for mismatched pretraining and downstream tasks.
• Studies performance dependence on statistical discrepancies between distributions.

Innovation

• Novel generalization bounds for domain shift
• Analysis of new label spaces in downstream tasks
• Performance guarantees for out-of-distribution classification