Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

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 r...

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Bibliographic Details
Published in:arXiv.org 2019-10
Main Authors: Hendrycks, Dan, Mantas Mazeika, Kadavath, Saurav, Song, Dawn
Format: Article
Language:English
Subjects:
Annotations
Data analysis
Robustness
Self-supervised learning
Supervised learning
Supervision
Uncertainty
Online Access:Get full text
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Summary: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.
ISSN:2331-8422