Anomaly Detection with Generative Adversarial Networks

Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft

Abstract: 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.

TL;DR: We propose a method for anomaly detection with GANs by searching the generator's latent space for good sample representations.

Keywords: Anomaly Detection, Generative Adversarial Networks, Deep Learning, Inverse Problems

Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [ImageNet](https://paperswithcode.com/dataset/imagenet)

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/anomaly-detection-with-generative-adversarial/code)