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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.00849 |
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| _version_ | 1866915592260288512 |
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| author | Huang, Zhexiao He, Weihao Deng, Shutao Chen, Junzhe Yuan, Chao Wang, Hongxin Zhou, Changsheng |
| author_facet | Huang, Zhexiao He, Weihao Deng, Shutao Chen, Junzhe Yuan, Chao Wang, Hongxin Zhou, Changsheng |
| contents | Out-of-distribution (OOD) detection is essential for deploying deep learning models in open-world environments. Existing approaches, such as energy-based scoring and gradient-projection methods, typically rely on high-dimensional representations to separate in-distribution (ID) and OOD samples. We introduce P-OCS (Perturbations in the Orthogonal Complement Subspace), a lightweight and theoretically grounded method that operates in the orthogonal complement of the principal subspace defined by ID features. P-OCS applies a single projected perturbation restricted to this complementary subspace, enhancing subtle ID-OOD distinctions while preserving the geometry of ID representations. We show that a one-step update is sufficient in the small-perturbation regime and provide convergence guarantees for the resulting detection score. Experiments across multiple architectures and datasets demonstrate that P-OCS achieves state-of-the-art OOD detection with negligible computational cost and without requiring model retraining, access to OOD data, or changes to model architecture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00849 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection Huang, Zhexiao He, Weihao Deng, Shutao Chen, Junzhe Yuan, Chao Wang, Hongxin Zhou, Changsheng Machine Learning Out-of-distribution (OOD) detection is essential for deploying deep learning models in open-world environments. Existing approaches, such as energy-based scoring and gradient-projection methods, typically rely on high-dimensional representations to separate in-distribution (ID) and OOD samples. We introduce P-OCS (Perturbations in the Orthogonal Complement Subspace), a lightweight and theoretically grounded method that operates in the orthogonal complement of the principal subspace defined by ID features. P-OCS applies a single projected perturbation restricted to this complementary subspace, enhancing subtle ID-OOD distinctions while preserving the geometry of ID representations. We show that a one-step update is sufficient in the small-perturbation regime and provide convergence guarantees for the resulting detection score. Experiments across multiple architectures and datasets demonstrate that P-OCS achieves state-of-the-art OOD detection with negligible computational cost and without requiring model retraining, access to OOD data, or changes to model architecture. |
| title | Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.00849 |