Efficient GAN-Based Anomaly Detection
This work addresses anomaly detection for applications like image analysis and network security, but it is incremental as it builds on recent GAN models.
The paper tackled the problem of anomaly detection using GANs, achieving state-of-the-art performance on image and network intrusion datasets with a several hundred-fold speed improvement at test time compared to existing methods.
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.