An Augmentation-Aware Theory for Self-Supervised Contrastive Learning

Self-supervised contrastive learning has emerged as a powerful tool to learn meaningful representations from unlabeled data. • However, in the existing theoretical research, the role of data augmentation is still under-exploited. • The effects of specific augmentation types such as random cropping and random color distortion are unexplained.