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arXiv · 2604.22360

Revisiting Neural Activation Coverage for Uncertainty Estimation

Benedikt Franke·Nils Förster·Frank Köster·Asja Fischer·Markus Lange·Arne Raulf
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Abstract

Neural activation coverage (NAC) is a recently-proposed technique for out-of-distribution detection and generalization. We build upon this promising foundation and extend the method to work as an uncertainty estimation technique for already-trained artificial neural networks in the domain of regression. Our experiments confirm NAC uncertainty scores to be more meaningful than other techniques, e.g. Monte-Carlo Dropout.

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Benedikt Franke, Nils Förster, Frank Köster, Asja Fischer, Markus Lange, Arne Raulf. 2026-04-24. Revisiting Neural Activation Coverage for Uncertainty Estimation. https://doi.org/10.14428/esann%2F2026.es2026-29

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