Self-supervised Learning is More Robust to Dataset Imbalance

Understand

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.