Title: Correcting Three Existing Beliefs on Mutual Information in Contrastive Learning. Abstract: Contrastive learning has played a pivotal role in the recent success of unsupervised representation learning. It has been commonly explained with instance discrimination and a mutual information loss, and some of the fundamental explanations are based on mutual information analysis. In this work, we develop new methods that enable rigorous analysis of mutual information in contrastive learning. Using the methods, we investigate three existing beliefs and show that they are incorrect. Based on the investigation results, we address two issues in the discussion section. In particular, we question if contrastive learning is indeed an unsupervised representation learning method because the current framework of contrastive learning relies on validation performance for tuning the augmentation design.