  Unsupervised learning of anomaly detection in high-dimensional data, such as
images, is a challenging problem recently subject to intense research. Through
careful modelling of the data distribution of normal samples, it is possible to
detect deviant samples, so called anomalies. Generative Adversarial Networks
(GANs) can model the highly complex, high-dimensional data distribution of
normal image samples, and have shown to be a suitable approach to the problem.
Previously published GAN-based anomaly detection methods often assume that
anomaly-free data is available for training. However, this assumption is not
valid in most real-life scenarios, a.k.a. in the wild. In this work, we
evaluate the effects of anomaly contaminations in the training data on
state-of-the-art GAN-based anomaly detection methods. As expected, detection
performance deteriorates. To address this performance drop, we propose to add
an additional encoder network already at training time and show that joint
generator-encoder training stratifies the latent space, mitigating the problem
with contaminated data. We show experimentally that the norm of a query image
in this stratified latent space becomes 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, previously
untested dataset with cell images.
