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

• URL: http://arxiv.org/abs/2302.02628v1
• Date: Mon, 6 Feb 2023 08:57:20 GMT
• Title: Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness
• Authors: Ailin Deng, Shen Li, Miao Xiong, Zhirui Chen, and Bryan Hooi
• Abstract summary: We introduce a new approach of self-supervised probing, which enables us to check and mitigate the overconfidence issue for a trained model. We provide a simple yet effective framework, which can be flexibly applied to existing trustworthiness-related methods in a plug-and-play manner.
• Score: 29.320691367586004
• License: http://creativecommons.org/licenses/by/4.0/
• 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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