Nearest Neighbor Guidance for Out-of-Distribution Detection
Jaewoo Park Yoon Gyo Jung Andrew Beng Jin Teoh
Detecting out-of-distribution (OOD) samples are crucial for machine learning
models deployed in open-world environments. Classifier-based scores are a
standard approach for OOD detection due to their fine-grained detection
capability. However, these scores often suffer from overconfidence issues,
misclassifying OOD samples distant from the in-distribution region. To address
this challenge, we propose a method called Nearest Neighbor Guidance (NNGuide)
that guides the classifier-based score to respect the boundary geometry of the
data manifold. NNGuide reduces the overconfidence of OOD samples while
preserving the fine-grained capability of the classifier-based score. We
conduct extensive experiments on ImageNet OOD detection benchmarks under
diverse settings, including a scenario where the ID data undergoes natural
distribution shift. Our results demonstrate that NNGuide provides a significant
performance improvement on the base detection scores, achieving
state-of-the-art results on both AUROC, FPR95, and AUPR metrics. The code is
given at \url{https://github.com/roomo7time/nnguide}.