mlsanityData to 7 Oct 2026

Fence GAN: Towards Better Anomaly Detection

arXiv 1904.01209 ↗First posted 2 Apr 2019cs.LG stat.MLcited by 32

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Abstract (arXiv)

Anomaly detection is a classical problem where the aim is to detect anomalous data that do not belong to the normal data distribution. Current state-of-the-art methods for anomaly detection on complex high-dimensional data are based on the generative adversarial network (GAN). However, the traditional GAN loss is not directly aligned with the anomaly detection objective: it encourages the distribution of the generated samples to overlap with the real data and so the resulting discriminator has been found to be ineffective as an anomaly detector. In this paper, we propose simple modifications to the GAN loss such that the generated samples lie at the boundary of the real data distribution. With our modified GAN loss, our anomaly detection method, called Fence GAN (FGAN), directly uses the discriminator score as an anomaly threshold. Our experimental results using the MNIST, CIFAR10 and KDD99 datasets show that Fence GAN yields the best anomaly classification accuracy compared to state-of-the-art methods.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Apr 2019.

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Citing papers per year: 2021: 14; 2022: 5; 2023: 5; 2024: 3; 2025: 5.

32 papers.

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  1. 2021Deep Graph-level Anomaly Detection by Glocal Knowledge Distillationcited by 67
  2. 2024Generative Semi-supervised Graph Anomaly Detectioncited by 22
  3. 2021Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Reviewcited by 15
  4. 2022Towards Unsupervised Open World Semantic Segmentationcited by 13
  5. 2021MemStream: Memory-Based Streaming Anomaly Detectioncited by 12
  6. 2022Functional Anomaly Detection: a Benchmark Studycited by 10
  7. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
  8. 2022Machine Learning in NextG Networks via Generative Adversarial Networkscited by 8
  9. 2023Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptorscited by 8
  10. 2021Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shiftscited by 7
  11. 2022Raising the Bar on the Evaluation of Out-of-Distribution Detectioncited by 7
  12. 2021Elsa: Energy-based learning for semi-supervised anomaly detectioncited by 6
20 more
  1. 2021Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundarycited by 6
  2. 2021Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formationcited by 6
  3. 2021Efficient Anomaly Detection Using Self-Supervised Multi-Cue Taskscited by 6
  4. 2023Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Surveycited by 5
  5. 2021Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Trainingcited by 4
  6. 2025Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detectioncited by 4
  7. 2021Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generatorscited by 3
  8. 2023unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Featurescited by 3
  9. 2023GADY: Unsupervised Anomaly Detection on Dynamic Graphscited by 3
  10. 2021GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphscited by 2
  11. 2021OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundarycited by 2
  12. 2024Targeted Augmented Data for Audio Deepfake Detectioncited by 2
  13. 2025AutoML for Multi-Class Anomaly Compensation of Sensor Driftcited by 2
  14. 2024Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysiscited by 1
  15. 2025ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detectioncited by 1
  16. 2021New Perspective on Progressive GANs Distillation for One-class Novelty Detectioncited by 0
  17. 2021Anomaly Detection using Capsule Networks for High-dimensional Datasetscited by 0
  18. 2022Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translationcited by 0
  19. 2025Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactionscited by 0
  20. 2025Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Imagescited by 0