通过正交补空间扰动，高效区分分布内与分布外样本。
Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection
- 在主成分的正交补空间中施加单步扰动，增强识别能力。
- 无需重训练、不需外部数据，在多个模型上达到最优性能。
- 计算开销极低，适合实际部署场景。
分布外（OOD）检测对于开放世界中的深度学习模型部署至关重要。现有方法如基于能量的评分和梯度投影法通常依赖高维表示来区分分布内（ID）与分布外样本。本文提出P-OCS（正交补空间扰动），一种轻量且理论严谨的方法，其在由分布内特征定义的主子空间的正交补空间中操作。P-OCS在该互补子空间中施加单一投影扰动，强化了细微的分布内-外差异，同时保持分布内表示的几何结构。我们证明在小扰动条件下一步更新即足够，并为所得检测分数提供收敛保证。在多个架构和数据集上的实验表明，P-OCS在几乎无额外计算成本下实现当前最优的分布外检测性能，且无需模型重训练、无需访问分布外数据或修改模型结构。
原文摘要 · Abstract (English)
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.
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