mlsanityData to 14 Oct 2026

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

arXiv 1805.06725 ↗First posted 17 May 2018cs.CVcited by 409

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Abstract (arXiv)

Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this can be addressed as a supervised learning problem, a significantly more challenging problem is that of detecting the unknown/unseen anomaly case that takes us instead into the space of a one-class, semi-supervised learning paradigm. We introduce such a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image. The use of the additional encoder network maps this generated image to its latent representation. Minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution - an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches.

Key facts. Cited by 409 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 May 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 82; 2022: 87; 2023: 75; 2024: 76; 2025: 56; 2026: 33.

409 papers.

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  1. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  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. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  5. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
  6. 2022ADBench: Anomaly Detection Benchmarkcited by 232
  7. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  8. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 146
  9. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 139
  10. 2021OpenGAN: Open-Set Recognition via Open Data Generationcited by 135
  11. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  12. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
38 more
  1. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 117
  2. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 99
  3. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 93
  4. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  5. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 90
  6. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 89
  7. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  8. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 82
  9. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80
  10. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  11. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  12. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  13. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  14. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  15. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 68
  16. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  17. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  18. 2023ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstructioncited by 63
  19. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 57
  20. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  21. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  22. 2021Deep Metric Learning for Open World Semantic Segmentationcited by 51
  23. 2022SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakescited by 49
  24. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  25. 2023DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detectioncited by 46
  26. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  27. 2023TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detectioncited by 43
  28. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  29. 2023Diversity-Measurable Anomaly Detectioncited by 41
  30. 2021One-Class Classification: A Surveycited by 35
  31. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  32. 2022Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Imagescited by 35
  33. 2023Anomaly Detection under Distribution Shiftcited by 35
  34. 2021SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detectioncited by 34
  35. 2023Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detectioncited by 32
  36. 2022ADTR: Anomaly Detection Transformer with Feature Reconstructioncited by 31
  37. 2023ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognitioncited by 31
  38. 2024Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detectioncited by 28

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