Generative adversarial networks (GANs) are able to model the complex highdimensional 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.
@article{arxiv.1802.06222,
title = {Efficient GAN-Based Anomaly Detection},
author = {Houssam Zenati and Chuan Sheng Foo and Bruno Lecouat and Gaurav Manek and Vijay Ramaseshan Chandrasekhar},
journal= {arXiv preprint arXiv:1802.06222},
year = {2019}
}
Comments
Updated version of this work is published at ICDM 2018, see arXiv:1812.02288 . Submitted to the ICLR Workshop 2018