Abstract
We present the largest systematic comparison to date of out-of-distribution (OOD) detection methods using AURC and AUGRC as primary metrics. Our comparison explores different regimes of distribution shift (stratified by CLIP embeddings of the out-of-distribution image datasets) with varying numbers of classes and uses a representation-centric view of OOD detection, including neural collapse metrics, for subsequent analysis. Together the empirical results and representation analysis provides novel insights and statistically grounded guidance for method selection under distribution shift. Experiments cover two representation paradigms: CNNs trained from scratch and a fine-tuned Vision Transformer (ViT), evaluated on CIFAR-10/100, SuperCIFAR-100, and TinyImageNet. Using a multiple-comparison-controlled, rank-based pipeline (Friedman test with Conover-Holm post-hoc) and Bron-Kerbosch cliques, we find that the learned feature space largely determines OOD efficacy. For both CNNs and ViTs, probabilistic scores (e.g., MSR, GEN) dominate misclassification (ID) detection. Under stronger shifts, geometry-aware scores (e.g., NNGuide, fDBD, CTM) prevail on CNNs, whereas on ViTs GradNorm and KPCA Reconstruction Error remain consistently competitive. We further show a class-count-dependent trade-off for Monte-Carlo Dropout (MCD) and that a simple PCA projection improves several detectors. The neural-collapse-based geometric analysis explains when prototype and boundary-based scores become optimal under strong shifts.