Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables

Yu Gui; Cong Ma; Zongming Ma

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arXiv · 2505.12473

Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables

Yu Gui·Cong Ma·Zongming Ma

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Abstract

Multi-modal contrastive learning as a self-supervised representation learning technique has achieved great success in foundation model training, such as CLIP~\citep{radford2021learning}. In this paper, we study the theoretical properties of the learned representations from multi-modal contrastive learning beyond linear representations and specific data distributions. Our analysis reveals that, enabled by temperature optimization, multi-modal contrastive learning not only maximizes mutual information between modalities but also adapts to intrinsic dimensions of data, which can be much lower than user-specified dimensions for representation vectors. Experiments on both synthetic and real-world datasets demonstrate the ability of contrastive learning to learn low-dimensional and informative representations, bridging theoretical insights and practical performance.

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BibTeX RIS

Yu Gui, Cong Ma, Zongming Ma. 2025-05-18. Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables. https://arxiv.org/abs/2505.12473

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