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Out-of-Distribution Detection Based on Total Variation Estimation

arXiv 2601.15867 ↗First posted 22 Jan 2026cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 22 references that are in the map. First posted on arXiv 22 Jan 2026.

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19 of 22 references.

References · 19

  1. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  2. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  3. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  4. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  5. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  6. 2019AugMix: A Simple Data Processing Method to Improve Robustness and Uncertaintycited by 897
  7. 2023GLIGEN: Open-Set Grounded Text-to-Image Generationcited by 774
  8. 2018MINE: Mutual Information Neural Estimationcited by 680
  9. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  10. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  11. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  12. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 404
7 more
  1. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 338
  2. 2022Mitigating Neural Network Overconfidence with Logit Normalizationcited by 251
  3. 2022Extremely Simple Activation Shaping for Out-of-Distribution Detectioncited by 212
  4. 2021OpenGAN: Open-Set Recognition via Open Data Generationcited by 135
  5. 2022RankFeat: Rank-1 Feature Removal for Out-of-distribution Detectioncited by 52
  6. 2023Balanced Energy Regularization Loss for Out-of-distribution Detectioncited by 16
  7. 2022On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detectioncited by 14

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