  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.
