用总变差分数区分数据分布，提升模型部署安全性
Out-of-Distribution Detection Based on Total Variation Estimation
- 基于总变差网络估计器计算输入对整体变差的贡献
- 在图像分类任务中各项指标均达领先水平
- 适合关注模型鲁棒性与异常检测的研究者
本文提出一种新型方法——基于总变差估计的分布外检测（TV-OOD），以应对实际应用中潜在的数据分布偏移问题。现有方法虽已取得良好效果，但TV-OOD通过总变差网络估计器量化每个输入对整体总变差的贡献，定义为总变差得分，从而有效区分分布内与分布外数据。该方法在多种模型和数据集上进行了测试，在图像分类任务中，各项评估指标均表现优异，结果或优于、或至少可媲美当前最先进的分布外检测技术。
原文摘要 · Abstract (English)
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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