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arXiv · 2601.15867

Out-of-Distribution Detection Based on Total Variation Estimation

Dabiao Ma·Zhiba Su·Jian Yang·Haojun Fei
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Abstract

This paper introduces a novel approach to securing machine learning model deployments against potential distribution shifts in practical applications, the Total Variation Out-of-Distribution (TV-OOD) detection method. Existing methods have produced satisfactory results, but TV-OOD improves upon these by leveraging the Total Variation Network Estimator to calculate each input's contribution to the overall total variation. By defining this as the total variation score, TV-OOD discriminates between in- and out-of-distribution data. The method's efficacy was tested across a range of models and datasets, consistently yielding results in image classification tasks that were either comparable or superior to those achieved by leading-edge out-of-distribution detection techniques across all evaluation metrics.

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Dabiao Ma, Zhiba Su, Jian Yang, Haojun Fei. 2026-01-22. Out-of-Distribution Detection Based on Total Variation Estimation. https://arxiv.org/abs/2601.15867

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