  Self-supervised Learning (SSL) including the mainstream contrastive learning
has achieved great success in learning visual representations without data
annotations. However, most of methods mainly focus on the instance level
information (\ie, the different augmented images of the same instance should
have the same feature or cluster into the same class), but there is a lack of
attention on the relationships between different instances. In this paper, we
introduced a novel SSL paradigm, which we term as relational self-supervised
learning (ReSSL) framework that learns representations by modeling the
relationship between different instances. Specifically, our proposed method
employs sharpened distribution of pairwise similarities among different
instances as \textit{relation} metric, which is thus utilized to match the
feature embeddings of different augmentations. Moreover, to boost the
performance, we argue that weak augmentations matter to represent a more
reliable relation, and leverage momentum strategy for practical efficiency.
Experimental results show that our proposed ReSSL significantly outperforms the
previous state-of-the-art algorithms in terms of both performance and training
efficiency. Code is available at \url{https://github.com/KyleZheng1997/ReSSL}.
