Evaluating Uncertainty Calibration for Open-Set Recognition

• URL: http://arxiv.org/abs/2205.07160v1
• Date: Sun, 15 May 2022 02:08:35 GMT
• Title: Evaluating Uncertainty Calibration for Open-Set Recognition
• Authors: Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi
• Abstract summary: Deep neural networks (DNNs) suffer from providing overconfident probabilities on out-of-distribution (OOD) data. We evaluate popular calibration techniques for open-set conditions in a way that is distinctly different from the conventional evaluation of calibration methods on OOD data.
• Score: 5.8022510096020525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite achieving enormous success in predictive accuracy for visual classification problems, deep neural networks (DNNs) suffer from providing overconfident probabilities on out-of-distribution (OOD) data. Yet, accurate uncertainty estimation is crucial for safe and reliable robot autonomy. In this paper, we evaluate popular calibration techniques for open-set conditions in a way that is distinctly different from the conventional evaluation of calibration methods on OOD data. Our results show that closed-set DNN calibration approaches are much less effective for open-set recognition, which highlights the need to develop new DNN calibration methods to address this problem.

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