Unified Multi-Layer Subspace Modeling for Cross-Domain OOD Detection

• URL: http://arxiv.org/abs/2609.06785v1
• Date: Sun, 06 Sep 2026 19:00:54 GMT
• Title: Unified Multi-Layer Subspace Modeling for Cross-Domain OOD Detection
• Abstract summary: PRISM is a model-agnostic post-hoc OOD detection method.<n>It fuses intermediate and deep features into a single hierarchical embedding.<n>It captures both in-subspace semantic deviations and off-subspace anomalies.
• Score: 12.06155749281499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-Distribution (OOD) detection remains a fundamental challenge for neural networks, whose predictions can be overconfident on inputs that deviate from the training distribution. Most post-hoc OOD detection methods derive scores from a single representation level (eg., logits or penultimate features) or combine multiple layers via depth selection or OOD-calibrated weighting. However, because OOD shifts are diverse, the most informative representation level can vary strongly across OOD types and domains, making fixed-layer choices and OOD-calibrated aggregation brittle. In this paper, we propose PRISM (Projected Representation with Intermediate-layer Subspace Modeling), a model-agnostic post-hoc OOD detection method that models a unified multi-layer feature representation rather than aggregating independently scored layers. PRISM fuses intermediate and deep features into a single hierarchical embedding, estimates an in-distribution (ID) principal subspace, and then combines two complementary signals: (i) a class-conditional Mahalanobis distance in the projected subspace and (ii) the residual energy orthogonal to the learned manifold. This simple design avoids OOD-tuned layer weighting while capturing both in-subspace semantic deviations and off-subspace anomalies. Across diverse benchmarks spanning natural images, medical imaging, and industrial visual inspection, PRISM achieves consistent state-of-the-art cross-domain OOD detection performance with a single default configuration across all evaluated domains and architectures. We further show that PRISM incurs minimal inference overhead, making it practical for real-world deployment.

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