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.

翻译：贝叶斯非参数方法天然适用于分布外检测问题。然而，现有研究大多回避这些技术，转而采用基于数据点预训练或学习嵌入之间距离的简化方法。本文揭示了贝叶斯非参数模型与相对马氏距离评分（一种常用的OOD检测方法）之间的形式化关联。基于此关联，我们提出具有分层先验的贝叶斯非参数混合模型，该模型推广了RMDS方法。我们在OpenOOD检测基准上评估这些模型，结果表明贝叶斯非参数方法能够改进现有OOD检测技术，尤其在训练类协方差结构存在差异且每类数据点相对较少的场景中表现突出。