mlsanityData to 7 Oct 2026

Towards Total Recall in Industrial Anomaly Detection

arXiv 2106.08265 ↗First posted 15 Jun 2021cs.CVcited by 772

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Cited by 44 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Being able to spot defective parts is a critical component in large-scale industrial manufacturing. A particular challenge that we address in this work is the cold-start problem: fit a model using nominal (non-defective) example images only. While handcrafted solutions per class are possible, the goal is to build systems that work well simultaneously on many different tasks automatically. The best performing approaches combine embeddings from ImageNet models with an outlier detection model. In this paper, we extend on this line of work and propose PatchCore, which uses a maximally representative memory bank of nominal patch-features. PatchCore offers competitive inference times while achieving state-of-the-art performance for both detection and localization. On the challenging, widely used MVTec AD benchmark PatchCore achieves an image-level anomaly detection AUROC score of up to 99.6%, more than halving the error compared to the next best competitor. We further report competitive results on two additional datasets and also find competitive results in the few samples regime.freefootnote^* Work done during a research internship at Amazon AWS. Code: github.com/amazon-research/patchcore-inspection.

Key facts. Cited by 772 papers in the map (data through 7 Oct 2026). Builds on 36 of its 61 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

36 of 61 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  10. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  11. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  12. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
24 more
  1. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  2. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  3. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  4. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  5. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
  6. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  7. 2019Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNetcited by 260
  8. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  9. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  10. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  11. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  12. 2019XRAI: Better Attributions Through Regionscited by 116
  13. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  14. 2016Deep Structured Energy Based Models for Anomaly Detectioncited by 113
  15. 2019Towards Visually Explaining Variational Autoencoderscited by 113
  16. 2020Deep Nearest Neighbor Anomaly Detectioncited by 103
  17. 2018Generative Probabilistic Novelty Detection with Adversarial Autoencoderscited by 99
  18. 2020Revisiting Training Strategies and Generalization Performance in Deep Metric Learningcited by 99
  19. 2020Iterative energy-based projection on a normal data manifold for anomaly localizationcited by 47
  20. 2019Small-GAN: Speeding Up GAN Training Using Core-setscited by 40
  21. 2019Data-Independent Neural Pruning via Coresetscited by 35
  22. 2018Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?cited by 17
  23. 2019Attribute Restoration Framework for Anomaly Detectioncited by 16
  24. 2020Unsupervised Anomaly Detection for X-Ray Imagescited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 51; 2023: 125; 2024: 189; 2025: 217; 2026: 185.

772 papers.

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  1. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  2. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  3. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 270
  4. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 267
  5. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  6. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  7. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
  8. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  9. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
  10. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  11. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  12. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
38 more
  1. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  2. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  3. 2023APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADcited by 128
  4. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 126
  5. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
  6. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  7. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  8. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  9. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 110
  10. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 97
  11. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  12. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 92
  13. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
  14. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  15. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  16. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  17. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  18. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  19. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  20. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 74
  21. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  22. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  23. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 72
  24. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  25. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  26. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  27. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  28. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  29. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  30. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  31. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  32. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 62
  33. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
  34. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  35. 2023Label-Free Liver Tumor Segmentationcited by 56
  36. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  37. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 55
  38. 2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55

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