  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 resorting 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 the support of different
intra-class samples will become more overlapped under aggressive data
augmentations, thus simply aligning the positive samples (augmented views of
the same sample) could make contrastive learning cluster intra-class samples
together. Based on this augmentation overlap perspective, theoretically, we
obtain asymptotically closed bounds for downstream performance under weaker
assumptions, and empirically, we propose an unsupervised model selection metric
ARC that aligns well with downstream accuracy. Our theory suggests an
alternative understanding of contrastive learning: the role of aligning
positive samples is more like a surrogate task than an ultimate goal, and the
overlapped augmented views (i.e., the chaos) create a ladder for contrastive
learning to gradually learn class-separated representations. The code for
computing ARC is available at https://github.com/zhangq327/ARC.
