Out-of-distribution (OOD) detection is essential for the safe deployment of
AI. Particularly, OOD detectors should generalize effectively across diverse
scenarios. To improve upon the generalizability of existing OOD detectors, we
introduce a highly versatile OOD detector, called Neural Collapse inspired OOD
detector (NC-OOD). We extend the prevalent observation that in-distribution
(ID) features tend to form clusters, whereas OOD features are far away.
Particularly, based on the recent observation, Neural Collapse, we further
demonstrate that ID features tend to cluster in proximity to weight vectors.
From our extended observation, we propose to detect OOD based on feature
proximity to weight vectors. To further rule out OOD samples, we leverage the
observation that OOD features tend to reside closer to the origin than ID
features. Extensive experiments show that our approach enhances the
generalizability of existing work and can consistently achieve state-of-the-art
OOD detection performance across a wide range of OOD Benchmarks over different
classification tasks, training losses, and model architectures.

基于 Neural Collapse 观察的特征向量相似度，我们提出了一种高度灵活的 Neural Collapse inspired OOD 检测器（NC-OOD），通过利用 ID 特征聚类性和 OOD 特征与原点的距离性，显著提高了现有工作的泛化能力，同时在不同分类任务、训练损失和模型架构的广泛实验中，始终实现了最新的 OOD 检测性能。