mlsanityData to 14 Oct 2026

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

arXiv 1703.05921 ↗First posted 17 Mar 2017cs.CV cs.LGcited by 566

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Cited by 34 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci.

Key facts. Cited by 566 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Mar 2017.

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Citing papers per year: 2021: 142; 2022: 121; 2023: 112; 2024: 95; 2025: 69; 2026: 27.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
  2. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  3. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  4. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405
  5. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  6. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  7. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  8. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 227
  9. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  10. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  11. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 154
  12. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 146
38 more
  1. 2021OpenGAN: Open-Set Recognition via Open Data Generationcited by 135
  2. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  3. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  4. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  5. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 111
  6. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  7. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  8. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 99
  9. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  10. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  11. 2023Self-Supervised Video Forensics by Audio-Visual Anomaly Detectioncited by 85
  12. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  13. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 82
  14. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80
  15. 2022Variational Model Inversion Attackscited by 75
  16. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74
  17. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 73
  18. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  19. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  20. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  21. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 68
  22. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  23. 2023Few-Shot Defect Image Generation via Defect-Aware Feature Manipulationcited by 67
  24. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  25. 2021CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detectioncited by 64
  26. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 61
  27. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  28. 2023Unsupervised Out-of-Distribution Detection with Diffusion Inpaintingcited by 59
  29. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  30. 2022Latent Outlier Exposure for Anomaly Detection with Contaminated Datacited by 48
  31. 2022TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysiscited by 48
  32. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 48
  33. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  34. 2023Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Needcited by 46
  35. 2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detectioncited by 46
  36. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  37. 2021Anomaly Detection in Medical Imaging -- A Mini Reviewcited by 44
  38. 2021Neural Contextual Anomaly Detection for Time Seriescited by 43

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