Fence GAN: Towards Better Anomaly Detection

Understand

Anomaly detection is a classical problem where the aim is to detect anomalous data that do not belong to the normal data distribution.

• Current state-of-the-art methods for anomaly detection on complex high-dimensional data are based on the generative adversarial network (GAN).
• However, the traditional GAN loss is not directly aligned with the anomaly detection objective: it encourages the distribution of the generated samples to overlap with the real data and so the resulting discriminator has been found to be ineffective as an anomaly detector.
• In this paper, we propose simple modifications to the GAN loss such that the generated samples lie at the boundary of the real data distribution.