Efficient GAN-Based Anomaly Detection

Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar

Abstract:

Generative adversarial networks (GANs) are able to model the complex high-dimensional
distributions of real-world data, which suggests they could be effective
for anomaly detection. However, few works have explored the use of GANs
for the anomaly detection task. We leverage recently developed GAN models for
anomaly detection, and achieve state-of-the-art performance on image and network
intrusion datasets, while being several hundred-fold faster at test time than
the only published GAN-based method.

Keywords: Anomaly Detection, Generative Adversarial Networks, Unsupervised Learning