One Score for All: Robust OOD Detection across Pretrained Models

Post-hoc out-of-distribution (OOD) detection should remain useful across pretrained classifiers and calibration budgets. In practice, however, changes in the pretrained model can reverse the in-distribution (ID)/OOD ordering of feature statistics, while class-relative references can become unreliable when estimated from few samples. We propose ONE, a common scoring and fitting procedure that uses pretrained class information both to combine detection evidence and to estimate its reference. Raw logits and Gaussian-form fit in normalized feature space are combined within corresponding classes before pooling. The resulting score retains each branch's overall evidence together with their soft class overlap, rather than discarding class correspondence through scalar fusion. We estimate the feature reference by shrinking empirical class centers toward classifier-derived priors according to sample counts and estimated mismatch, while regularizing covariance according to sample availability. The reference and separate evidence scales are fitted using labeled ID data, with the network frozen. Across nine benchmark settings, ONE achieves 86.3 mean AUROC with full calibration. On 30 public ImageNet checkpoints, it achieves 85.1 mean AUROC and a 6.8-point maximum gap to the best evaluated score on each checkpoint, improving both criteria over the evaluated alternatives. With one labeled ID calibration sample per class, it achieves higher mean AUROC (84.2) than the classifier-only GEN baseline (83.5).