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arXiv · 2302.02628

Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness

Ailin Deng·Shen Li·Miao Xiong·Zhirui Chen·Bryan Hooi
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Abstract

Trustworthy machine learning is of primary importance to the practical deployment of deep learning models. While state-of-the-art models achieve astonishingly good performance in terms of accuracy, recent literature reveals that their predictive confidence scores unfortunately cannot be trusted: e.g., they are often overconfident when wrong predictions are made, or so even for obvious outliers. In this paper, we introduce a new approach of self-supervised probing, which enables us to check and mitigate the overconfidence issue for a trained model, thereby improving its trustworthiness. We provide a simple yet effective framework, which can be flexibly applied to existing trustworthiness-related methods in a plug-and-play manner. Extensive experiments on three trustworthiness-related tasks (misclassification detection, calibration and out-of-distribution detection) across various benchmarks verify the effectiveness of our proposed probing framework.

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Ailin Deng, Shen Li, Miao Xiong, Zhirui Chen, Bryan Hooi. 2023-02-06. Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness. https://arxiv.org/abs/2302.02628

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