mlsanityData to 14 Oct 2026

Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey

arXiv 2207.10298 ↗First posted 21 Jul 2022cs.CVcited by 68

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Abstract (arXiv)

Currently, deep learning-based visual inspection has been highly successful with the help of supervised learning methods. However, in real industrial scenarios, the scarcity of defect samples, the cost of annotation, and the lack of a priori knowledge of defects may render supervised-based methods ineffective. In recent years, unsupervised anomaly localization algorithms have become more widely used in industrial inspection tasks. This paper aims to help researchers in this field by comprehensively surveying recent achievements in unsupervised anomaly localization in industrial images using deep learning. The survey reviews more than 120 significant publications covering different aspects of anomaly localization, mainly covering various concepts, challenges, taxonomies, benchmark datasets, and quantitative performance comparisons of the methods reviewed. In reviewing the achievements to date, this paper provides detailed predictions and analysis of several future research directions. This review provides detailed technical information for researchers interested in industrial anomaly localization and who wish to apply it to the localization of anomalies in other fields.

Key facts. Cited by 68 papers in the map (data through 14 Oct 2026). Builds on 63 of its 125 references that are in the map. First posted on arXiv 21 Jul 2022.

Builds on

63 of 125 references.

References · 63

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38 more
  1. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  2. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  3. 2019Deep Learning for Anomaly Detection: A Surveycited by 358
  4. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  5. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  6. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
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  8. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 227
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  12. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
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  20. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
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50 most cited of 63 All 63 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 17; 2024: 20; 2025: 14; 2026: 12.

68 papers.

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  1. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  2. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  3. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  4. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  5. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  6. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  7. 2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55
  8. 2024VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentationcited by 50
  9. 2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detectioncited by 46
  10. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  11. 2023Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detectioncited by 32
  12. 2025Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detectioncited by 31
38 more
  1. 2023Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstructioncited by 23
  2. 2022Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detectioncited by 18
  3. 2022FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detectioncited by 16
  4. 2023LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detectioncited by 13
  5. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  6. 2023REB: Reducing Biases in Representation for Industrial Anomaly Detectioncited by 10
  7. 2024Learning Multi-view Anomaly Detection with Efficient Adaptive Selectioncited by 9
  8. 2023High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysiscited by 7
  9. 2024Statistical Test for Anomaly Detections by Variational Auto-Encoderscited by 7
  10. 2024A SAM-guided Two-stream Lightweight Model for Anomaly Detectioncited by 7
  11. 2024ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detectioncited by 7
  12. 2024Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillationcited by 6
  13. 2022ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detectioncited by 5
  14. 2024Blind Localization and Clustering of Anomalies in Texturescited by 5
  15. 2023Brain-Inspired Spiking Neural Networks for Industrial Fault Diagnosis: A Survey, Challenges, and Opportunitiescited by 4
  16. 2025Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detectioncited by 4
  17. 2024IPAD: Industrial Process Anomaly Detection Datasetcited by 3
  18. 2024Synth4Seg -- Learning Defect Data Synthesis for Defect Segmentation using Bi-level Optimizationcited by 3
  19. 2025Towards High-Resolution Industrial Image Anomaly Detectioncited by 3
  20. 2026A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detectioncited by 3
  21. 2023Neural Architecture Search for Visual Anomaly Segmentationcited by 2
  22. 2024ALMRR: Anomaly Localization Mamba on Industrial Textured Surface with Feature Reconstruction and Refinementcited by 2
  23. 2024SPACE: SPAtial-aware Consistency rEgularization for anomaly detection in Industrial applicationscited by 2
  24. 2025Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perceptioncited by 2
  25. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  26. 2025Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detectioncited by 2
  27. 2026VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detectioncited by 2
  28. 2022Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniquescited by 1
  29. 2023FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detectioncited by 1
  30. 2023Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localizationcited by 1
  31. 2023Weakly Supervised Anomaly Detection for Chest X-Ray Imagecited by 1
  32. 2024Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detectioncited by 1
  33. 2024Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Industrial Anomaly Detectioncited by 1
  34. 2024Quantum Patch-Based Autoencoder for Anomaly Segmentationcited by 1
  35. 2024Domain-independent detection of known anomaliescited by 1
  36. 2024Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertaintycited by 1
  37. 2024Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detectioncited by 1
  38. 2024PaRCE: Probabilistic and Reconstruction-based Competency Estimation for CNN-based Image Classificationcited by 1

50 most cited of 68 All 68 (JSON)