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

TMLR Paper5747 Authors

Abstract: 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.

Submission Length: Regular submission (no more than 12 pages of main content)

Previous TMLR Submission Url: https://openreview.net/forum?id=1X1B27KBPR

Changes Since Last Submission: We have removed the Acknowledgements section since it violated the double blind policy.

Assigned Action Editor: ~Feng_Liu2

Submission Number: 5747