Title: Hierarchical Cross Contrastive Learning of Visual Representations. Abstract: The rapid progress of self-supervised learning (SSL) has greatly reduced the labeling cost in computer vision. The key idea of SSL is to learn invariant visual representations by maximizing the similarity between different views of the same input image. In most SSL methods, the representation invariant is measured by a contrastive loss which compares one of the network outputs after the projection head to its augmented version. Albeit being effective, this approach overlooks the information containing in the hidden layer of the projection head therefore could be sub-optimal. In this work,  we propose a novel approach termed Hierarchical Cross Contrastive Learning(HCCL) to further distill the information mismatched by the conventional contrastive loss. The HCCL uses a hierarchical projection head to project the raw representations of the backbone into multiple latent spaces and then compares latent features across different levels and different views. By cross-level contrastive learning, HCCL not only regulates invariant on multiple hidden levels but also crosses different levels, improving the generalization ability of the learned visual representations. As a simple and generic method, HCCL can be applied to different SSL frameworks. We validate the efficacy of HCCL under classification, detection, segmentation, and few-shot learning tasks. Extensive experimental results show that HCCL outperforms most previous methods in various benchmark datasets.