Chaos is a Ladder: A New Understanding of Contrastive Learning

Abstract

Recently, contrastive learning has risen to be a promising approach for large-scale self-supervised learning. However, theoretical understanding of how it works is still unclear. In this paper, we propose a new guarantee on the downstream performance without resort to the conditional independence assumption that is widely adopted in previous work but hardly holds in practice. Our new theory hinges on the insight that different samples from the same class could be bridged together with aggressive data augmentations, thus simply aligning the positive samples (augmented views of the same sample) could make contrastive learning cluster intra-class samples together. We also show that our theory aligns well with existing contrastive methods on both synthetic and real-world datasets. Our work suggests an alternative understanding of contrastive learning: the role of aligning positive samples is more like a surrogate task than an ultimate goal, and it is the overlapping augmented views (i.e., the chaos) that create a ladder for contrastive learning to gradually learn class-separated representations.

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