# Adversarially Learned Anomaly Detection

arXiv: https://arxiv.org/abs/1812.02288 · HTML: https://mlsanity.com/paper/1812.02288/

## Key facts

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

## Built on by (most cited first, 30 of 115)

- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Student-Teacher Feature Pyramid Matching for Anomaly Detection](https://mlsanity.com/paper/2103.04257/) (2021)
- [OpenGAN: Open-Set Recognition via Open Data Generation](https://mlsanity.com/paper/2104.02939/) (2021)
- [Self-Supervised Video Forensics by Audio-Visual Anomaly Detection](https://mlsanity.com/paper/2301.01767/) (2023)
- [Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection](https://mlsanity.com/paper/2207.01463/) (2022)
- [Explainable Deep Few-shot Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/2108.00462/) (2021)
- [Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection](https://mlsanity.com/paper/2105.10500/) (2021)
- [Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images](https://mlsanity.com/paper/2205.11474/) (2022)
- [The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider](https://mlsanity.com/paper/2105.14027/) (2021)
- [Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests](https://mlsanity.com/paper/2107.06929/) (2021)
- [Generative Semi-supervised Graph Anomaly Detection](https://mlsanity.com/paper/2402.11887/) (2024)
- [Unsupervised anomaly detection algorithms on real-world data: how many do we need?](https://mlsanity.com/paper/2305.00735/) (2023)
- [Multi-Class Data Description for Out-of-distribution Detection](https://mlsanity.com/paper/2104.00941/) (2021)
- [DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications](https://mlsanity.com/paper/2109.11495/) (2021)
- [Dense outlier detection and open-set recognition based on training with noisy negative images](https://mlsanity.com/paper/2101.09193/) (2021)
- [PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection](https://mlsanity.com/paper/2412.12154/) (2024)
- [ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2103.03664/) (2021)
- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [Towards Unsupervised Open World Semantic Segmentation](https://mlsanity.com/paper/2201.01073/) (2022)
- [Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images](https://mlsanity.com/paper/2403.08689/) (2024)
- [MemStream: Memory-Based Streaming Anomaly Detection](https://mlsanity.com/paper/2106.03837/) (2021)
- [Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning](https://mlsanity.com/paper/2104.11952/) (2021)
- [A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms](https://mlsanity.com/paper/2204.09825/) (2022)
- [Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs](https://mlsanity.com/paper/2204.13335/) (2022)
- [Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success](https://mlsanity.com/paper/2208.07734/) (2022)
- [Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection](https://mlsanity.com/paper/2210.07548/) (2022)
- [Open-Set Graph Anomaly Detection via Normal Structure Regularisation](https://mlsanity.com/paper/2311.06835/) (2023)
- [Making Reconstruction-based Method Great Again for Video Anomaly Detection](https://mlsanity.com/paper/2301.12048/) (2023)
- [Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks](https://mlsanity.com/paper/2303.01978/) (2023)
- [VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces](https://mlsanity.com/paper/2211.15775/) (2022)

## 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.