Is Complex Training Necessary for Long-Tailed OOD Detection? A Re-think from Feature Geometry

TL;DR

The paper introduces Hyperspherical Pooled Mahalanobis (HPM), a simple post-hoc method that improves long-tailed out-of-distribution detection by normalizing features and using a pooled covariance, challenging the necessity of complex training.

Contribution

It proposes HPM, a post-hoc feature normalization and covariance pooling technique, demonstrating significant improvements over raw Mahalanobis scoring in LT-OOD detection without complex training.

Findings

HPM improves AUROC scores significantly on CIFAR-LT datasets.

HPM achieves the best Log Efficiency Score (LES) on CIFAR-100-LT.

HPM retains high detection performance with lower training cost.

Abstract

Long-tailed out-of-distribution (LT-OOD) detection is often addressed with specialized training, including auxiliary out-of-distribution (OOD) data, abstention heads, contrastive objectives, energy losses, or gradient-conflict control. We show that these training mechanisms can obscure a simpler issue: frozen long-tailed representations may already contain useful OOD evidence, but raw Mahalanobis distance is distorted by frequency-coupled feature radius and poorly supported tail covariance. We propose Hyperspherical Pooled Mahalanobis (HPM), a post-hoc detector that normalizes features onto the unit sphere and replaces class-specific covariance with a pooled, ridge-regularized metric while keeping class means as semantic anchors. In CIFAR-LT experiments and an ImageNet-100-LT near-OOD boundary analysis, HPM improves raw Mahalanobis scoring; for Prior-Calibrated ERM (PC-ERM), it raises AUROC from 46.49 to 85.67 on CIFAR-10-LT and from 50.40 to 78.35 on CIFAR-100-LT. This simple PC-ERM+HPM pipeline also achieves the best Log Efficiency Score (LES; 3.08) on CIFAR-100-LT, retaining roughly 95% of the best CIFAR-100-LT AUROC observed among the compared post-hoc scores at substantially lower training-time cost. These results argue for evaluating representation quality, detector geometry, and training complexity as separate factors in LT-OOD detection.