mlsanityData to 7 Oct 2026

DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection

arXiv 2108.07610 ↗First posted 17 Aug 2021cs.CVcited by 510

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Cited by 41 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies. These methods are trained only on anomaly-free images, and often require hand-crafted post-processing steps to localize the anomalies, which prohibits optimizing the feature extraction for maximal detection capability. In addition to reconstructive approach, we cast surface anomaly detection primarily as a discriminative problem and propose a discriminatively trained reconstruction anomaly embedding model (DRAEM). The proposed method learns a joint representation of an anomalous image and its anomaly-free reconstruction, while simultaneously learning a decision boundary between normal and anomalous examples. The method enables direct anomaly localization without the need for additional complicated post-processing of the network output and can be trained using simple and general anomaly simulations. On the challenging MVTec anomaly detection dataset, DRAEM outperforms the current state-of-the-art unsupervised methods by a large margin and even delivers detection performance close to the fully-supervised methods on the widely used DAGM surface-defect detection dataset, while substantially outperforming them in localization accuracy.

Key facts. Cited by 510 papers in the map (data through 7 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 17 Aug 2021.

Builds on

18 of 34 references.

References · 18

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  3. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  4. 2013Describing Textures in the Wildcited by 2,110
  5. 2017Focal Loss for Dense Object Detectioncited by 749
  6. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  7. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  8. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  9. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  10. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  11. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  12. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
6 more
  1. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  2. 2019Towards Visually Explaining Variational Autoencoderscited by 113
  3. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  4. 2018Anomaly Detection using One-Class Neural Networkscited by 82
  5. 2020End-to-end training of a two-stage neural network for defect detectioncited by 12
  6. 2020Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoderscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 40; 2023: 87; 2024: 132; 2025: 132; 2026: 116.

510 papers.

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  1. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  2. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
  3. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 267
  4. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  5. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
  6. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  7. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  8. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  9. 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
  10. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  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
38 more
  1. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  2. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  3. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 110
  4. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 97
  5. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 92
  6. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 89
  7. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
  8. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  9. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  10. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  11. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  12. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 74
  13. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  14. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  15. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  16. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  17. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  18. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  19. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  20. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  21. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  22. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 62
  23. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
  24. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  25. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 56
  26. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  27. 2024A Closer Look at AUROC and AUPRC under Class Imbalancecited by 56
  28. 2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55
  29. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  30. 2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
  31. 2024Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detectioncited by 49
  32. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  33. 2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detectioncited by 46
  34. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  35. 2024MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detectioncited by 46
  36. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  37. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  38. 2023TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detectioncited by 43

50 most cited of 510 All 510 (JSON)