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arXiv · 2512.15492

Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier

Adrián Detavernier·Jasper De Bock
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Abstract

We consider two conceptually different approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We compare both approaches on a number of benchmark datasets and show that there is no clear winner between the two, but that they are complementary and can be combined to obtain a hybrid approach that outperforms both RQ and UQ. As a byproduct of our approach, for each dataset, we also obtain an assessment of the relative importance of uncertainty and robustness as sources of unreliability.

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Adrián Detavernier, Jasper De Bock. 2025-12-17. Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier. https://arxiv.org/abs/2512.15492

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