mlsanityData to 7 Oct 2026

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

arXiv 2011.08785 ↗First posted 17 Nov 2020cs.CVcited by 591

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Cited by 29 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding, and of multivariate Gaussian distributions to get a probabilistic representation of the normal class. It also exploits correlations between the different semantic levels of CNN to better localize anomalies. PaDiM outperforms current state-of-the-art approaches for both anomaly detection and localization on the MVTec AD and STC datasets. To match real-world visual industrial inspection, we extend the evaluation protocol to assess performance of anomaly localization algorithms on non-aligned dataset. The state-of-the-art performance and low complexity of PaDiM make it a good candidate for many industrial applications.

Key facts. Cited by 591 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 27; 2022: 60; 2023: 104; 2024: 132; 2025: 136; 2026: 132.

591 papers.

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  1. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  2. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  3. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  4. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  5. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  6. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  7. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
  8. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  9. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  10. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
  11. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  12. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
38 more
  1. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
  2. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  3. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
  4. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  5. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  6. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  7. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  8. 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
  9. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  10. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
  11. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
  12. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  13. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  14. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  15. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 97
  16. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  17. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 92
  18. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  19. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 89
  20. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
  21. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  22. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  23. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  24. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  25. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  26. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  27. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 72
  28. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  29. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  30. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  31. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  32. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  33. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  34. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  35. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  36. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 62
  37. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  38. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 56

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