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.

翻译：本文提出了一种新颖的方法，用于在实际应用中防范机器学习模型部署时可能出现的分布偏移，即全变差分布外检测方法。现有方法已取得令人满意的结果，但TV-OOD通过利用全变差网络估计器计算每个输入对整体全变差的贡献，对此进行了改进。通过将其定义为全变差得分，TV-OOD能够区分分布内与分布外数据。该方法的有效性在一系列模型和数据集上进行了测试，在图像分类任务中，所有评估指标上的结果均与最先进的分布外检测技术相当或更优。