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 https://github.com/roomo7time/nnguide.

检测开放环境中机器学习模型的异常样本是至关重要的。通过引入最近邻引导（NNGuide）方法，我们解决了由于边界几何导致的分类器评分过分自信的问题，同时保持了分类器评分的细粒度能力，并在多种设置下，包括 ID 数据自然分布变化的情况下，在 ImageNet 异常检测基准测试中取得了显著的性能提升，达到了 AUROC、FPR95 和 AUPR 指标的最佳结果。