Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Abstract

Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating or reducing the need for annotations. We find that self-supervision can benefit robustness in a variety of ways, including robustness to adversarial examples, label corruption, and common input corruptions. Additionally, self-supervision greatly benefits out-of-distribution detection on difficult, near-distribution outliers, so much so that it exceeds the performance of fully supervised methods. These results demonstrate the promise of self-supervision for improving robustness and uncertainty estimation and establish these tasks as new axes of evaluation for future self-supervised learning research.

Cite

Text

Hendrycks et al. "Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty." Neural Information Processing Systems, 2019.

Markdown

[Hendrycks et al. "Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty." Neural Information Processing Systems, 2019.](https://mlanthology.org/neurips/2019/hendrycks2019neurips-using/)

BibTeX

@inproceedings{hendrycks2019neurips-using,
  title     = {{Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty}},
  author    = {Hendrycks, Dan and Mazeika, Mantas and Kadavath, Saurav and Song, Dawn},
  booktitle = {Neural Information Processing Systems},
  year      = {2019},
  pages     = {15663-15674},
  url       = {https://mlanthology.org/neurips/2019/hendrycks2019neurips-using/}
}