ReSSL: Relational Self-Supervised Learning with Weak Augmentation

TL;DR

ReSSL introduces a novel self-supervised learning framework that models relationships between instances using pairwise similarity distributions, emphasizing weak augmentations and momentum strategies to improve visual representation learning.

Contribution

The paper proposes a relational SSL paradigm that focuses on inter-instance relationships, utilizing similarity distributions and weak augmentations for enhanced learning efficiency.

Findings

ReSSL outperforms state-of-the-art methods in accuracy.

ReSSL achieves higher training efficiency.

Relational modeling improves representation quality.

Abstract

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}.