mlsanityData to 13 Oct 2026

Deep Nearest Neighbor Anomaly Detection

arXiv 2002.10445 ↗First posted 24 Feb 2020cs.LG cs.CV stat.MLcited by 104

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Abstract (arXiv)

Nearest neighbors is a successful and long-standing technique for anomaly detection. Significant progress has been recently achieved by self-supervised deep methods (e.g. RotNet). Self-supervised features however typically under-perform Imagenet pre-trained features. In this work, we investigate whether the recent progress can indeed outperform nearest-neighbor methods operating on an Imagenet pretrained feature space. The simple nearest-neighbor based-approach is experimentally shown to outperform self-supervised methods in: accuracy, few shot generalization, training time and noise robustness while making fewer assumptions on image distributions.

Key facts. Cited by 104 papers in the map (data through 13 Oct 2026). First posted on arXiv 24 Feb 2020.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 16; 2022: 20; 2023: 23; 2024: 19; 2025: 14; 2026: 12.

104 papers.

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  1. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 774
  2. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 598
  3. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  4. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  5. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  6. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 59
  7. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  8. 2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
  9. 2021LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networkscited by 42
  10. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 41
  11. 2023Improving neural network representations using human similarity judgmentscited by 38
  12. 2022Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Imagescited by 35
38 more
  1. 2021Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localizationcited by 32
  2. 2023Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detectioncited by 32
  3. 2023ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detectioncited by 30
  4. 2024Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detectioncited by 28
  5. 2021Transformaly -- Two (Feature Spaces) Are Better Than Onecited by 25
  6. 2023MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detectioncited by 25
  7. 2024ResAD: A Simple Framework for Class Generalizable Anomaly Detectioncited by 25
  8. 2021CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesiscited by 24
  9. 2023Improving Selective Visual Question Answering by Learning from Your Peerscited by 24
  10. 2021Anomaly Detection via Self-organizing Mapcited by 18
  11. 2022Back to the Basics: Revisiting Out-of-Distribution Detection Baselinescited by 16
  12. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 16
  13. 2021Unsupervised Outlier Detection using Memory and Contrastive Learningcited by 11
  14. 2022Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentcited by 11
  15. 2022N-pad : Neighboring Pixel-based Industrial Anomaly Detectioncited by 11
  16. 2023UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detectioncited by 11
  17. 2023REB: Reducing Biases in Representation for Industrial Anomaly Detectioncited by 9
  18. 2024Universal Novelty Detection Through Adaptive Contrastive Learningcited by 9
  19. 2021Enhancing Unsupervised Anomaly Detection with Score-Guided Networkcited by 8
  20. 2023Model Selection of Anomaly Detectors in the Absence of Labeled Validation Datacited by 8
  21. 2024AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnosticscited by 8
  22. 2024Towards flexible perception with visual memorycited by 8
  23. 2023High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysiscited by 7
  24. 2025RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samplescited by 7
  25. 2026SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modelingcited by 7
  26. 2021Data Invariants to Understand Unsupervised Out-of-Distribution Detectioncited by 5
  27. 2024COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classificationcited by 5
  28. 2024Few-shot Online Anomaly Detection and Segmentationcited by 5
  29. 2025SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmarkcited by 5
  30. 2021Constrained Adaptive Projection with Pretrained Features for Anomaly Detectioncited by 4
  31. 2022ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variablescited by 4
  32. 2022Data refinement for fully unsupervised visual inspection using pre-trained networkscited by 4
  33. 2022Time Series Anomaly Detection via Reinforcement Learning-Based Model Selectioncited by 4
  34. 2022Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detectioncited by 4
  35. 2024Deep Generic Representations for Domain-Generalized Anomalous Sound Detectioncited by 4
  36. 2025Killing it with Zero-Shot: Adversarially Robust Novelty Detectioncited by 4
  37. 2025Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detectioncited by 4
  38. 2021Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspectioncited by 3

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