Self-supervised Learning is More Robust to Dataset Imbalance

TL;DR

This paper demonstrates that self-supervised learning (SSL) is inherently more robust to dataset imbalance than supervised learning, and introduces a re-weighted regularization method to further improve SSL performance on imbalanced datasets.

Contribution

It provides a systematic investigation of SSL under dataset imbalance, revealing its robustness and proposing a novel re-weighted regularization technique to enhance SSL representations.

Findings

SSL representations are more robust to class imbalance than supervised ones.

The performance gap between balanced and imbalanced SSL pre-training is smaller than in supervised learning.

A re-weighted regularization method improves SSL performance on imbalanced datasets.

Abstract

Self-supervised learning (SSL) is a scalable way to learn general visual representations since it learns without labels. However, large-scale unlabeled datasets in the wild often have long-tailed label distributions, where we know little about the behavior of SSL. In this work, we systematically investigate self-supervised learning under dataset imbalance. First, we find out via extensive experiments that off-the-shelf self-supervised representations are already more robust to class imbalance than supervised representations. The performance gap between balanced and imbalanced pre-training with SSL is significantly smaller than the gap with supervised learning, across sample sizes, for both in-domain and, especially, out-of-domain evaluation. Second, towards understanding the robustness of SSL, we hypothesize that SSL learns richer features from frequent data: it may learn label-irrelevant-but-transferable features that help classify the rare classes and downstream tasks. In contrast, supervised learning has no incentive to learn features irrelevant to the labels from frequent examples. We validate this hypothesis with semi-synthetic experiments and theoretical analyses on a simplified setting. Third, inspired by the theoretical insights, we devise a re-weighted regularization technique that consistently improves the SSL representation quality on imbalanced datasets with several evaluation criteria, closing the small gap between balanced and imbalanced datasets with the same number of examples.