[2510.15202] A Geometry-Based View of Mahalanobis OOD Detection

Computer Science > Machine Learning arXiv:2510.15202 (cs) [Submitted on 17 Oct 2025 (v1), last revised 3 Mar 2026 (this version, v3)] Title:A Geometry-Based View of Mahalanobis OOD Detection Authors:Denis Janiak, Jakub Binkowski, Tomasz Kajdanowicz View a PDF of the paper titled A Geometry-Based View of Mahalanobis OOD Detection, by Denis Janiak and 2 other authors View PDF HTML (experimental) Abstract:Out-of-distribution (OOD) detection is critical for reliable deployment of vision models. Mahalanobis-based detectors remain strong baselines, yet their performance varies widely across modern pretrained representations, and it is unclear which properties of a feature space cause these methods to succeed or fail. We conduct a large-scale study across diverse foundation-model backbones and Mahalanobis variants. First, we show that Mahalanobis-style OOD detection is not universally reliable: performance is highly representation-dependent and can shift substantially with pretraining data and fine-tuning regimes. Second, we link this variability to in-distribution geometry and identify a two-term ID summary that consistently tracks Mahalanobis OOD behavior across detectors: within-class spectral structure and local intrinsic dimensionality. Finally, we treat normalization as a geometric control mechanism and introduce radially scaled $\ell_2$ normalization, $\phi_\beta(z)=z/\|z\|^\beta$, which preserves directions while contracting or expanding feature radii. Varying $\beta$ cha...