mlsanityData to 14 Oct 2026

Nearest Neighbor Guidance for Out-of-Distribution Detection

arXiv 2309.14888 ↗First posted 26 Sep 2023cs.CVcited by 64

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Abstract (arXiv)

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 urlhttps://github.com/roomo7time/nnguide.

Key facts. Cited by 64 papers in the map (data through 14 Oct 2026). Builds on 38 of its 49 references that are in the map. First posted on arXiv 26 Sep 2023.

Builds on

38 of 49 references.

References · 38

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  4. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  5. 2013Describing Textures in the Wildcited by 2,125
  6. 2019Do ImageNet Classifiers Generalize to ImageNet?cited by 1,515
  7. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  8. 2019Natural Adversarial Examplescited by 1,408
  9. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  10. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  11. 2020Designing Network Design Spacescited by 960
  12. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
26 more
  1. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
  2. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  3. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  4. 2020Measuring Robustness to Natural Distribution Shifts in Image Classificationcited by 454
  5. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405
  6. 2021Open-Set Recognition: a Good Closed-Set Classifier is All You Need?cited by 353
  7. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 339
  8. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  9. 2021Exploring the Limits of Out-of-Distribution Detectioncited by 283
  10. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 268
  11. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  12. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  13. 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
  14. 2021DICE: Leveraging Sparsification for Out-of-Distribution Detectioncited by 166
  15. 2018Reducing Network Agnostophobiacited by 165
  16. 2021Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalizationcited by 144
  17. 2020Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentationcited by 133
  18. 2022Domain Generalization by Mutual-Information Regularization with Pre-trained Modelscited by 133
  19. 2022How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?cited by 128
  20. 2022Is Out-of-Distribution Detection Learnable?cited by 109
  21. 2021Evidential Deep Learning for Open Set Action Recognitioncited by 96
  22. 2021Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentationcited by 77
  23. 2022RankFeat: Rank-1 Feature Removal for Out-of-distribution Detectioncited by 52
  24. 2022Boosting Out-of-distribution Detection with Typical Featurescited by 48
  25. 2021OODformer: Out-Of-Distribution Detection Transformercited by 27
  26. 2021Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Drivingcited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 19; 2025: 27; 2026: 17.

64 papers.

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  1. 2024Outlier detection by ensembling uncertainty with negative objectnesscited by 34
  2. 2025Mahalanobis++: Improving OOD Detection via Feature Normalizationcited by 32
  3. 2024Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advancescited by 26
  4. 2024Kernel PCA for Out-of-Distribution Detectioncited by 24
  5. 2025Reliable Few-shot Learning under Dual Noisescited by 17
  6. 2025OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detectioncited by 14
  7. 2024Gradient-Regularized Out-of-Distribution Detectioncited by 13
  8. 2024Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Modelscited by 13
  9. 2024Selective Classification Under Distribution Shiftscited by 12
  10. 2024Forte : Finding Outliers with Representation Typicality Estimationcited by 9
  11. 2024A Unified Approach Towards Active Learning and Out-of-Distribution Detectioncited by 7
  12. 2024OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learningcited by 7
38 more
  1. 2025AdaSCALE: Adaptive Scaling for OOD Detectioncited by 7
  2. 2024Generalized Source Tracing: Detecting Novel Audio Deepfake Algorithm with Real Emphasis and Fake Dispersion Strategycited by 5
  3. 2024WeiPer: OOD Detection using Weight Perturbations of Class Projectionscited by 4
  4. 2024Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networkscited by 4
  5. 2024What If the Input is Expanded in OOD Detection?cited by 4
  6. 2024PViT: Prior-augmented Vision Transformer for Out-of-distribution Detectioncited by 3
  7. 2025Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimationcited by 3
  8. 2025Enhancing Out-of-Distribution Detection with Extended Logit Normalizationcited by 3
  9. 2026Trust The Typicalcited by 3
  10. 2024Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectorscited by 2
  11. 2024Out-of-Distribution Detection with Relative Anglescited by 2
  12. 2025OpenEarthSensing: Large-Scale Fine-Grained Benchmark for Open-World Remote Sensingcited by 2
  13. 2025STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainabilitycited by 2
  14. 2025Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detectioncited by 2
  15. 2025Activation Subspaces for Out-of-Distribution Detectioncited by 2
  16. 2025Intra-Class Probabilistic Embeddings for Uncertainty Estimation in Vision-Language Modelscited by 2
  17. 2023RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-distribution Detectioncited by 1
  18. 2024ESCAPE: Energy-based Selective Adaptive Correction for Out-of-distribution 3D Human Pose Estimationcited by 1
  19. 2025DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detectioncited by 1
  20. 2025Hypercone Assisted Contour Generation for Out-of-Distribution Detectioncited by 1
  21. 2025Harnessing Feature Resonance under Arbitrary Target Alignment for Out-of-Distribution Node Detectioncited by 1
  22. 2025SphOR: A Representation Learning Perspective on Open-set Recognition for Identifying Unknown Classes in Deep Learning Modelscited by 1
  23. 2025Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximationcited by 1
  24. 2025Balanced Hyperbolic Embeddings Are Natural Out-of-Distribution Detectorscited by 1
  25. 2025Membership Inference Attack with Partial Featurescited by 1
  26. 2025Nearest-Class Mean and Logits Agreement for Wildlife Open-Set Recognitioncited by 1
  27. 2025SupLID: Geometrical Guidance for Out-of-Distribution Detection in Semantic Segmentationcited by 1
  28. 2026One Model, Many Behaviors: Training-Induced Effects on Out-of-Distribution Detectioncited by 1
  29. 2026Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysiscited by 1
  30. 2026MahaVar: OOD Detection via Class-wise Mahalanobis Distance Variance under Neural Collapsecited by 1
  31. 2026UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shiftscited by 1
  32. 2026MARVEL: Margin-Aware Robust von Mises-Fischer Expert Learning for Long-Tailed Out-of-Distribution Detectioncited by 1
  33. 2024Beyond Perceptual Distance: Discrepancy Assessment on Deep Representation for Out-of-Distribution Detection with Diffusion Modelcited by 0
  34. 2024RICASSO: Reinforced Imbalance Learning with Class-Aware Self-Supervised Outliers Exposurecited by 0
  35. 2025TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Datacited by 0
  36. 2025Feature Bank Enhancement for Distance-based Out-of-Distribution Detectioncited by 0
  37. 2025COSTARR: Consolidated Open Set Technique with Attenuation for Robust Recognitioncited by 0
  38. 2025Multi-Method Ensemble for Out-of-Distribution Detectioncited by 0

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