This paper introduces Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that combines contrastive learning with clustering. PCL not only learns low-level features for instance discrimination but also encodes semantic structures discovered by clustering into the learned embedding space. The method introduces prototypes as latent variables to help find the maximum-likelihood estimation of network parameters in an Expectation-Maximization (EM) framework. The E-step involves finding the distribution of prototypes via clustering, while the M-step optimizes the network via contrastive learning. The proposed ProtoNCE loss, a generalized version of the InfoNCE loss, encourages representations to be closer to their assigned prototypes. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks, particularly in low-resource transfer learning. The code and pre-trained models are available at <https://github.com/salesforce/PCL>.This paper introduces Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that combines contrastive learning with clustering. PCL not only learns low-level features for instance discrimination but also encodes semantic structures discovered by clustering into the learned embedding space. The method introduces prototypes as latent variables to help find the maximum-likelihood estimation of network parameters in an Expectation-Maximization (EM) framework. The E-step involves finding the distribution of prototypes via clustering, while the M-step optimizes the network via contrastive learning. The proposed ProtoNCE loss, a generalized version of the InfoNCE loss, encourages representations to be closer to their assigned prototypes. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks, particularly in low-resource transfer learning. The code and pre-trained models are available at <https://github.com/salesforce/PCL>.