mlsanityData to 14 Oct 2026

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

arXiv 2110.12076 ↗First posted 22 Oct 2021cs.LGcited by 15

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Abstract (arXiv)

Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumours. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: a lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted a great deal of attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic literature review of the applications of GANs in anomaly detection, covering 128 papers on the subject. The goal of this review paper is to analyze and summarize: (1) which anomaly detection techniques can benefit from certain types of GANs, and how, (2) in which application domains GAN-assisted anomaly detection techniques have been applied, and (3) which datasets and performance metrics have been used to evaluate these techniques. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 74 of its 229 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

74 of 229 references.

References · 74

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  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
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38 more
  1. 2017Generative Adversarial Networks: An Overviewcited by 590
  2. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  3. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
  4. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 464
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  9. 2016Adversarial Feature Learningcited by 350
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  12. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  13. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  14. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  15. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  16. 2017Generalization and Equilibrium in Generative Adversarial Nets (GANs)cited by 166
  17. 2016Semantic Image Inpainting with Deep Generative Modelscited by 157
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  19. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  20. 2016Energy-based Generative Adversarial Networkcited by 119
  21. 2018Adversarially Learned Anomaly Detectioncited by 115
  22. 2016Deep Structured Energy Based Models for Anomaly Detectioncited by 114
  23. 2020A Survey on Generative Adversarial Networks: Variants, Applications, and Trainingcited by 97
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  26. 2017Abnormal Event Detection in Videos using Generative Adversarial Netscited by 82
  27. 2015Learning Deep Representations of Appearance and Motion for Anomalous Event Detectioncited by 75
  28. 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
  29. 2017Adversarial Feature Matching for Text Generationcited by 58
  30. 2017Dual Discriminator Generative Adversarial Netscited by 55
  31. 2020MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstructioncited by 50
  32. 2020G2D: Generate to Detect Anomalycited by 42
  33. 2018DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GANcited by 34
  34. 2020Any-Shot Sequential Anomaly Detection in Surveillance Videoscited by 34
  35. 2019Fence GAN: Towards Better Anomaly Detectioncited by 32
  36. 2021Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detectioncited by 31
  37. 2018An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detectioncited by 30
  38. 2017Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowdscited by 28

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 2; 2025: 7; 2026: 1.

15 papers.

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  1. 2024Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Surveycited by 9
  2. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  3. 2025Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inferencecited by 3
  4. 2025Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Reviewcited by 2
  5. 2022VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surfacecited by 1
  6. 2023Advancements In Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-The-Artcited by 1
  7. 2025Contextual Learning for Anomaly Detection in Tabular Datacited by 1
  8. 2025Generative Model Predictive Control in Manufacturing Processes: A Reviewcited by 1
  9. 2022Feature anomaly detection system (FADS) for intelligent manufacturingcited by 0
  10. 2023Anomaly Detection in Power Generation Plants with Generative Adversarial Networkscited by 0
  11. 2023Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detectioncited by 0
  12. 2024Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfacescited by 0
3 more
  1. 2025Investigating Mask-aware Prototype Learning for Tabular Anomaly Detectioncited by 0
  2. 2025WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networkscited by 0
  3. 2026Refining Decision Boundaries In Anomaly Detection Using Similarity Search Within the Feature Spacecited by 0