A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection

ArXi:2502.08695v2 Announce Type: replace-cross 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.