Nearest Neighbor Guidance for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2309.14888v1
• Date: Tue, 26 Sep 2023 12:40:35 GMT
• Title: Nearest Neighbor Guidance for Out-of-Distribution Detection
• Authors: Jaewoo Park, Yoon Gyo Jung, Andrew Beng Jin Teoh
• Abstract summary: We propose Nearest Neighbor Guidance (NNGuide) for detecting out-of-distribution (OOD) samples. NNGuide reduces the overconfidence of OOD samples while preserving the fine-grained capability of the classifier-based score. Our results demonstrate that NNGuide provides a significant performance improvement on the base detection scores.
• Score: 18.851275688720108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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