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.
@article{arxiv.2601.15867,
title = {Out-of-Distribution Detection Based on Total Variation Estimation},
author = {Dabiao Ma and Zhiba Su and Jian Yang and Haojun Fei},
journal= {arXiv preprint arXiv:2601.15867},
year = {2026}
}