  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.
