mlsanityData to 14 Oct 2026

Adversarially Learned Anomaly Detection

arXiv 1812.02288 ↗First posted 6 Dec 2018cs.LG stat.MLcited by 115

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Abstract (arXiv)

Anomaly detection is a significant and hence well-studied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge. As Generative Adversarial Networks (GANs) are able to model the complex high-dimensional distributions of real-world data, they offer a promising approach to address this challenge. In this work, we propose an anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based on bi-directional GANs, that derives adversarially learned features for the anomaly detection task. ALAD then uses reconstruction errors based on these adversarially learned features to determine if a data sample is anomalous. ALAD builds on recent advances to ensure data-space and latent-space cycle-consistencies and stabilize GAN training, which results in significantly improved anomaly detection performance. ALAD achieves state-of-the-art performance on a range of image and tabular datasets while being several hundred-fold faster at test time than the only published GAN-based method.

Key facts. Cited by 115 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Dec 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 31; 2022: 23; 2023: 31; 2024: 14; 2025: 11; 2026: 5.

115 papers.

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  1. 2022ADBench: Anomaly Detection Benchmarkcited by 232
  2. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 146
  3. 2021OpenGAN: Open-Set Recognition via Open Data Generationcited by 135
  4. 2023Self-Supervised Video Forensics by Audio-Visual Anomaly Detectioncited by 85
  5. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  6. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  7. 2021Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detectioncited by 42
  8. 2022Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Imagescited by 35
  9. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 29
  10. 2021Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Testscited by 24
  11. 2024Generative Semi-supervised Graph Anomaly Detectioncited by 22
  12. 2023Unsupervised anomaly detection algorithms on real-world data: how many do we need?cited by 20
38 more
  1. 2021Multi-Class Data Description for Out-of-distribution Detectioncited by 19
  2. 2021DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applicationscited by 18
  3. 2021Dense outlier detection and open-set recognition based on training with noisy negative imagescited by 17
  4. 2024PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selectioncited by 17
  5. 2021ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentationcited by 15
  6. 2021Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Reviewcited by 15
  7. 2022Towards Unsupervised Open World Semantic Segmentationcited by 13
  8. 2024Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Imagescited by 13
  9. 2021MemStream: Memory-Based Streaming Anomaly Detectioncited by 12
  10. 2021Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learningcited by 11
  11. 2022A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithmscited by 11
  12. 2022Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANscited by 11
  13. 2022Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Successcited by 11
  14. 2022Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detectioncited by 11
  15. 2023Open-Set Graph Anomaly Detection via Normal Structure Regularisationcited by 11
  16. 2023Making Reconstruction-based Method Great Again for Video Anomaly Detectioncited by 10
  17. 2023Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networkscited by 10
  18. 2022VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Tracescited by 9
  19. 2023Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Imagescited by 9
  20. 2023Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Modelscited by 9
  21. 2023METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detectioncited by 9
  22. 2021Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Reviewcited by 8
  23. 2023Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptorscited by 8
  24. 2021Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shiftscited by 7
  25. 2021A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methodscited by 7
  26. 2023Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videoscited by 7
  27. 2023Anomaly Detection with Score Distribution Discriminationcited by 7
  28. 2024The OPS-SAT benchmark for detecting anomalies in satellite telemetrycited by 7
  29. 2021Elsa: Energy-based learning for semi-supervised anomaly detectioncited by 6
  30. 2022HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Diseasecited by 6
  31. 2022Multi-Task Learning based Video Anomaly Detection with Attentioncited by 6
  32. 2025Subject Information Extraction for Novelty Detection with Domain Shiftscited by 6
  33. 2022Adversarial Support Alignmentcited by 5
  34. 2022Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detectioncited by 5
  35. 2023Transparent Anomaly Detection via Concept-based Explanationscited by 5
  36. 2023MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Networkcited by 5
  37. 2024Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Productscited by 5
  38. 2025Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepanciescited by 5

50 most cited of 115 All 115 (JSON)