Demystifying How Self-Supervised Features Improve Training from Noisy Labels

Hao Cheng, Zhaowei Zhu, Xing Sun, Yang Liu

Keywords: Learning with noisy labels, Self-Supervised Learning

Abstract: The advancement of self-supervised learning (SSL) motivates researchers to apply SSL on other tasks such as learning with noisy labels. Recent literature indicates that methods built on SSL features can substantially improve the performance of learning with noisy labels. Nonetheless, the deeper reasons why (and how) SSL features benefit the training from noisy labels are less understood. In this paper, we study why and how self-supervised features help networks resist label noise using both theoretical analyses and numerical experiments. Our result shows that, given a quality encoder pre-trained from SSL, a simple linear layer trained by the cross-entropy loss is theoretically robust to symmetric label noise. Further, we provide insights for how knowledge distilled from SSL features can alleviate the over-fitting problem. We hope our work provides a better understanding for learning with noisy labels from the perspective of self-supervised learning and can potentially serve as a guideline for further research.

One-sentence Summary: Our work provides theoretical analyses and experiments to show why and how self-supervised features improve training from noisy labels.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2110.09022/code)