  Mahalanobis distance (MD) is a simple and popular post-processing method for
detecting out-of-distribution (OOD) inputs in neural networks. We analyze its
failure modes for near-OOD detection and propose a simple fix called relative
Mahalanobis distance (RMD) which improves performance and is more robust to
hyperparameter choice. On a wide selection of challenging vision, language, and
biology OOD benchmarks (CIFAR-100 vs CIFAR-10, CLINC OOD intent detection,
Genomics OOD), we show that RMD meaningfully improves upon MD performance (by
up to 15% AUROC on genomics OOD).
