  Out-of-distribution (OOD) detection is a critical task for deploying machine
learning models in the open world. Distance-based methods have demonstrated
promise, where testing samples are detected as OOD if they are relatively far
away from in-distribution (ID) data. However, prior methods impose a strong
distributional assumption of the underlying feature space, which may not always
hold. In this paper, we explore the efficacy of non-parametric nearest-neighbor
distance for OOD detection, which has been largely overlooked in the
literature. Unlike prior works, our method does not impose any distributional
assumption, hence providing stronger flexibility and generality. We demonstrate
the effectiveness of nearest-neighbor-based OOD detection on several benchmarks
and establish superior performance. Under the same model trained on
ImageNet-1k, our method substantially reduces the false positive rate
(FPR@TPR95) by 24.77% compared to a strong baseline SSD+, which uses a
parametric approach Mahalanobis distance in detection. Code is available:
https://github.com/deeplearning-wisc/knn-ood.
