Title: Inverse Optimal Transport with Application to Contrastive Learning. Abstract: Previous works in contrastive learning (CL) mainly focus on pairwise views to learn the representations by attracting the positive samples and repelling negative ones. In this work, we understand the CL with a collective point set matching view and solve this problem with the formulation of inverse optimal transport(IOT), which is a min-min optimization to learn the features. By varying the relaxation degree of constraints in inner minimization of IOT, one can naturally get three different contrastive losses and reveal that InfoNCE is a special case of them, which shows a new and more generalized understanding view of CL. Besides, with our soft matching view, a uniformity penalty is also proposed to improve the representation learning. Experimental results show the effectiveness of our methods.