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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.15867 |
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| _version_ | 1866908781586153472 |
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| author | Ma, Dabiao Su, Zhiba Yang, Jian Fei, Haojun |
| author_facet | Ma, Dabiao Su, Zhiba Yang, Jian Fei, Haojun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15867 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Out-of-Distribution Detection Based on Total Variation Estimation Ma, Dabiao Su, Zhiba Yang, Jian Fei, Haojun Computer Vision and Pattern Recognition 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. |
| title | Out-of-Distribution Detection Based on Total Variation Estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.15867 |