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arXiv · 1802.06222

Efficient GAN-Based Anomaly Detection

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

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.

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Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar. 2018-02-17. Efficient GAN-Based Anomaly Detection. https://arxiv.org/abs/1802.06222

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