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Efficient GAN-Based Anomaly Detection

arXiv 1802.06222 ↗First posted 17 Feb 2018cs.LG stat.MLcited by 122

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Abstract (arXiv)

Generative adversarial networks (GANs) are able to model the complex highdimensional distributions of real-world data, which suggests they could be effective for anomaly detection. However, few works have explored the use of GANs for the anomaly detection task. We leverage recently developed GAN models for anomaly detection, and achieve state-of-the-art performance on image and network intrusion datasets, while being several hundred-fold faster at test time than the only published GAN-based method.

Key facts. Cited by 122 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2018.

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Citing papers per year: 2021: 40; 2022: 29; 2023: 24; 2024: 16; 2025: 10; 2026: 3.

122 papers.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  2. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  3. 2021OpenGAN: Open-Set Recognition via Open Data Generationcited by 135
  4. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  5. 2022MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiescited by 68
  6. 2022Self-Supervised Masking for Unsupervised Anomaly Detection and Localizationcited by 43
  7. 2021LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networkscited by 42
  8. 2022MAEDAY: MAE for few and zero shot AnomalY-Detectioncited by 30
  9. 2021A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videoscited by 28
  10. 2021A Survey on Anomaly Detection for Technical Systems using LSTM Networkscited by 27
  11. 2021Transformaly -- Two (Feature Spaces) Are Better Than Onecited by 25
  12. 2021CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesiscited by 24
38 more
  1. 2022Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solutioncited by 24
  2. 2021Distribution-Aware Testing of Neural Networks Using Generative Modelscited by 22
  3. 2021DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detectioncited by 21
  4. 2023Deep Learning in Deterministic Computational Mechanicscited by 21
  5. 2021Deep Clustering based Fair Outlier Detectioncited by 19
  6. 2021Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systemscited by 19
  7. 2022Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Modelcited by 18
  8. 2023Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approachcited by 17
  9. 2021ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentationcited by 15
  10. 2023Learning Energy-Based Prior Model with Diffusion-Amortized MCMCcited by 13
  11. 2023RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detectioncited by 12
  12. 2021Visual Anomaly Detection for Images: A Surveycited by 11
  13. 2022Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANscited by 11
  14. 2022Benchmarking Unsupervised Anomaly Detection and Localizationcited by 11
  15. 2022Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Textcited by 11
  16. 2022Deep Contrastive One-Class Time Series Anomaly Detectioncited by 10
  17. 2023SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classificationcited by 10
  18. 2021Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learningcited by 9
  19. 2023Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Imagescited by 9
  20. 2023Precursor-of-Anomaly Detection for Irregular Time Seriescited by 9
  21. 2023Energy Discrepancies: A Score-Independent Loss for Energy-Based Modelscited by 9
  22. 2021Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Reviewcited by 8
  23. 2022Machine Learning in NextG Networks via Generative Adversarial Networkscited by 8
  24. 2023Learning Joint Latent Space EBM Prior Model for Multi-layer Generatorcited by 8
  25. 2023Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detectioncited by 8
  26. 2024"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihoodcited by 8
  27. 2021A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methodscited by 7
  28. 2022CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AIcited by 7
  29. 2023A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inferencecited by 7
  30. 2023Anomaly Detection with Score Distribution Discriminationcited by 7
  31. 2021Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundarycited by 6
  32. 2021Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formationcited by 6
  33. 2022PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentationcited by 6
  34. 2022HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Diseasecited by 6
  35. 2023Learning Hierarchical Features with Joint Latent Space Energy-Based Priorcited by 6
  36. 2022ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detectioncited by 5
  37. 2023Transparent Anomaly Detection via Concept-based Explanationscited by 5
  38. 2024Using Deep Convolutional Neural Networks to Detect Rendered Glitches in Video Gamescited by 5

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