Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training

Anomaly detection in high-dimensional data, such as images, is a challenging problem recently subject to intense research. Generative Adversarial Networks (GANs) have the ability to model the normal data distribution and, therefore, detect anomalies. Previously published GAN-based methods often assume that anomaly-free data is available for training. However, in real-life scenarios, this is not always the case. In this work, we examine the effects of contaminating training data with anomalies for state-of-the-art GAN-based anomaly detection methods. As expected, detection performance is reduced. To mitigate this problem, we propose to add an additional encoder network already at training time to adjust the structure of the latent space. As we show in our experiments, the distance in latent space from a query image to the origin is a highly significant cue to discriminate anomalies from normal data. The proposed method achieves state-of-the-art performance on CIFAR-10 as well as on a large new dataset with cell images.