Alignment and Uniformity on the Hypersphere: How Geometric Losses Govern Contrastive Representation Learning
Alignment and Uniformity on the Hypersphere: The Geometric Foundations of Contrastive Representation Learning Contrastive representation learning serves as the foundational objective behind modern neural embeddings, powering dense retrieval systems, visual-language models such as CLIP, and metric learning pipelines. While early literature justified contrastive learning through the InfoMax principle (maximizing mutual information between augmented views), theoretical and empirical analyses have
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