A Survey on GANs for Anomaly Detection

TL;DR

This paper surveys GAN-based methods for anomaly detection, evaluates their performance across datasets, and provides an open-source toolbox to facilitate further research in this area.

Contribution

It offers a comprehensive review of GAN-based anomaly detection techniques, empirically validates key models, and releases a public toolbox for the community.

Findings

GANs achieve remarkable results in anomaly detection

Empirical validation of main GAN models across datasets

Open source toolbox released for research and application

Abstract

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.