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arXiv · 2205.07160

Evaluating Uncertainty Calibration for Open-Set Recognition

Zongyao Lyu·Nolan B. Gutierrez·William J. Beksi
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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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Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi. 2022-05-15. Evaluating Uncertainty Calibration for Open-Set Recognition. https://arxiv.org/abs/2205.07160

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