A Survey on GANs for Anomaly Detection
This is an incremental survey that organizes and tests existing methods for researchers and practitioners in anomaly detection.
The paper surveys GAN-based methods for anomaly detection, providing empirical validation of main models, additional experimental results across datasets, and releasing an open-source toolbox.
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.