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Understanding Contrastive Learning via Gaussian Mixture Models
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Theoretically analyzes contrastive learning for dimensionality reduction in Gaussian Mixture Models to explain why it works well.
Problem
Understanding why contrastive learning, which pulls each unlabeled sample toward its augmentation and pushes it away from others, performs well, studied in a simple GMM dimensionality reduction setting.
Approach
Analyzes contrastive learning theoretically on a natural GMM dimensionality reduction problem, contrasting it with the standard GMM-based analysis setting, rather than relying on empirical or generic assumptions.
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