Before revision:  Theoretically, we prove that contrastive learning using ReLU networks provably learns the desired sparse features if proper augmentations are adopted.
After revision:  Theoretically, we prove that contrastive learning using \mathbf{ReLU networks provably learns the desired sparse features if proper augmentations are adopted.