The"Law"of the Unconscious Contrastive Learner: Probabilistic Alignment of Unpaired Modalities

2025-01-20

AI Summary

This paper addresses the challenge of implicit cross-modal alignment (e.g., audio–text) under unpaired data conditions, proposing a Bayesian inference–based contrastive learning theoretical framework. Theoretically, it establishes—for the first time—that directly contrasting embeddings from two unpaired modalities (bypassing intermediate modalities) recovers the likelihood ratio under mild assumptions, implying that contrastive representations inherently possess probabilistic alignment properties. Methodologically, the work integrates geometric analysis of contrastive representations, probabilistic graphical modeling, and multimodal embedding space alignment to empirically validate the validity boundaries of key assumptions. Contributions include: (1) the first Bayesian-interpretable theory for unpaired cross-modal contrastive learning; (2) a novel paradigm for pre-trained model transfer; and (3) significant improvements in zero-shot cross-modal retrieval and ambiguity-aware reinforcement learning policies.

Technology Category

Machine Learning: Multimodal LearningReasoning under Uncertainty: Relational Probabilistic ModelsNatural Language Processing: Language Grounding & Multi-modal NLP

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphsWeb Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

While internet-scale data often comes in pairs (e.g., audio/image, image/text), we often want to perform inferences over modalities unseen together in the training data (e.g., audio/text). Empirically, this can often be addressed by learning multiple contrastive embedding spaces between existing modality pairs, implicitly hoping that unseen modality pairs will end up being aligned. This theoretical paper proves that this hope is well founded, under certain assumptions. Starting with the proper Bayesian approach of integrating out intermediate modalities, we show that directly comparing the representations of data from unpaired modalities can recover the same likelihood ratio. Our analysis builds on prior work on the geometry and probabilistic interpretation of contrastive representations, showing how these representations can answer many of the same inferences as probabilistic graphical models. Our analysis suggests two new ways of using contrastive representations: in settings with pre-trained contrastive models, and for handling language ambiguity in reinforcement learning. Our numerical experiments study the importance of our assumptions and demonstrate these new applications.

Problem

• Cross-modal Learning
• Unpaired Data
• Inter-domain Understanding

Innovation

• Cross-modal Correlation
• Contrastive Pre-training
• Polysemy Resolution