Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images. Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks. Given a sample under consideration, our method is based on searching for a good representation of that sample in the latent space of the generator; if such a representation is not found, the sample is deemed anomalous.   We achieve state-of-the-art performance on standard image benchmark datasets and visual inspection of the most anomalous samples reveals that our method does indeed return anomalies.
We propose a method for anomaly detection with GANs by searching the generator's latent space for good sample representations. The authors propose using GAN for anomaly detection, a gradient-descent based method to iteratively update latent representations, and a novel parameter update to the generators. A GAN based approach to doing anomaly detection for image data where the generator's latent space is explored to find a representation for a test image.