# A Survey on GANs for Anomaly Detection

arXiv: https://arxiv.org/abs/1906.11632 · HTML: https://mlsanity.com/paper/1906.11632/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 30 of 32)

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- [CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis](https://mlsanity.com/paper/2103.06242/) (2021)
- [DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection](https://mlsanity.com/paper/2106.05410/) (2021)
- [Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation](https://mlsanity.com/paper/2105.04184/) (2021)
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- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods](https://mlsanity.com/paper/2111.01604/) (2021)
- [Game of GANs: Game-Theoretical Models for Generative Adversarial Networks](https://mlsanity.com/paper/2106.06976/) (2021)
- [ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN](https://mlsanity.com/paper/2308.06663/) (2023)
- [We Need to Rethink Benchmarking in Anomaly Detection](https://mlsanity.com/paper/2507.15584/) (2025)
- [PANDA : Perceptually Aware Neural Detection of Anomalies](https://mlsanity.com/paper/2104.13702/) (2021)
- [GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2105.10213/) (2021)
- [Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators](https://mlsanity.com/paper/2108.00664/) (2021)
- [Unsupervised Surrogate Anomaly Detection](https://mlsanity.com/paper/2504.20733/) (2025)
- [Leveraging 3D Information in Unsupervised Brain MRI Segmentation](https://mlsanity.com/paper/2101.10674/) (2021)
- [GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs](https://mlsanity.com/paper/2106.15504/) (2021)
- [Margin-Aware Intra-Class Novelty Identification for Medical Images](https://mlsanity.com/paper/2108.00117/) (2021)
- [Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning](https://mlsanity.com/paper/2205.04222/) (2022)
- [Quantifying the uncertainty of model-based synthetic image quality metrics](https://mlsanity.com/paper/2504.03623/) (2025)
- [Integration of deep generative Anomaly Detection algorithm in high-speed industrial line](https://mlsanity.com/paper/2603.07577/) (2026)
- [Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection](https://mlsanity.com/paper/2402.14384/) (2024)
- [Quantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detection](https://mlsanity.com/paper/2510.26487/) (2025)
- [Driving Anomaly Detection Using Conditional Generative Adversarial Network](https://mlsanity.com/paper/2203.08289/) (2022)
- [Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors](https://mlsanity.com/paper/2204.08159/) (2022)
- [Anomaly Detection via Autoencoder Composite Features and NCE](https://mlsanity.com/paper/2502.01920/) (2025)
- [A digital eye-fixation biomarker using a deep anomaly scheme to classify Parkisonian patterns](https://mlsanity.com/paper/2502.17762/) (2025)
- [Adventurer: Exploration with BiGAN for Deep Reinforcement Learning](https://mlsanity.com/paper/2503.18612/) (2025)
- [Comparative Study of Generative Models for Early Detection of Failures in Medical Devices](https://mlsanity.com/paper/2505.04845/) (2025)
- [A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networks](https://mlsanity.com/paper/2506.00420/) (2025)
- [Dirty and Clean-Label attack detection using GAN discriminators](https://mlsanity.com/paper/2506.01224/) (2025)

## Abstract (arXiv)

Anomaly detection is a significant problem faced in several research areas. Detecting and correctly classifying something unseen as anomalous is a challenging problem that has been tackled in many different manners over the years. Generative Adversarial Networks (GANs) and the adversarial training process have been recently employed to face this task yielding remarkable results. In this paper we survey the principal GAN-based anomaly detection methods, highlighting their pros and cons. Our contributions are the empirical validation of the main GAN models for anomaly detection, the increase of the experimental results on different datasets and the public release of a complete Open Source toolbox for Anomaly Detection using GANs.