CORE: Robust Out-of-Distribution Detection via Confidence and Orthogonal Residual Scoring

TL;DR

CORE introduces a novel out-of-distribution detection method that disentangles confidence and residual signals in features, leading to more robust and architecture-independent detection performance.

Contribution

The paper proposes CORE, a method that separates confidence and residual signals in features for improved OOD detection across diverse architectures and datasets.

Findings

Achieves state-of-the-art or competitive performance on multiple benchmarks.

Ranks first in three out of five evaluated settings.

Provides robust detection with negligible computational overhead.

Abstract

Out-of-distribution (OOD) detection is essential for deploying deep learning models reliably, yet no single method performs consistently across architectures and datasets -- a scorer that leads on one benchmark often falters on another. We attribute this inconsistency to a shared structural limitation: logit-based methods see only the classifier's confidence signal, while feature-based methods attempt to measure membership in the training distribution but do so in the full feature space where confidence and membership are entangled, inheriting architecture-sensitive failure modes. We observe that penultimate features naturally decompose into two orthogonal subspaces: a classifier-aligned component encoding confidence, and a residual the classifier discards. We discover that this residual carries a class-specific directional signature for in-distribution data -- a membership signal invisible to logit-based methods and entangled with noise in feature-based methods. We propose CORE (COnfidence + REsidual), which disentangles the two signals by scoring each subspace independently and combines them via normalized summation. Because the two signals are orthogonal by construction, their failure modes are approximately independent, producing robust detection where either view alone is unreliable. CORE achieves competitive or state-of-the-art performance across five architectures and five benchmark configurations, ranking first in three of five settings and achieving the highest grand average AUROC with negligible computational overhead.