用贝叶斯非参数模型改进分布外检测，提升小样本和异协方差场景下的效果。
A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection
- 基于层次先验的贝叶斯混合模型，扩展了相对马氏距离评分
- 在OpenOOD上优于现有方法，尤其在小样本和协方差差异大时
- 适合处理数据少、类别协方差不一致的分布外检测任务
贝叶斯非参数方法天然适用于分布外（OOD）检测问题。然而，这类方法常被更简单的基于预训练或学习嵌入点间距离的方法取代。本文揭示了贝叶斯非参数模型与相对马氏距离评分（RMDS）之间的形式关系，并在此基础上提出具有层次先验的贝叶斯非参数混合模型，以推广RMDS。我们在OpenOOD基准上评估这些模型，结果表明，在训练类别协方差结构差异大且每类数据点较少的情况下，贝叶斯非参数方法可显著优于现有技术。
原文摘要 · Abstract (English)
Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-trained or learned embeddings of data points. Here we show a formal relationship between Bayesian nonparametric models and the relative Mahalanobis distance score (RMDS), a commonly used method for OOD detection. Building on this connection, we propose Bayesian nonparametric mixture models with hierarchical priors that generalize the RMDS. We evaluate these models on the OpenOOD detection benchmark and show that Bayesian nonparametric methods can improve upon existing OOD methods, especially in regimes where training classes differ in their covariance structure and where there are relatively few data points per class.
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