  We propose a novel reconstruction-based model for anomaly detection, called
Y-GAN. The model consists of a Y-shaped auto-encoder and represents images in
two separate latent spaces. The first captures meaningful image semantics, key
for representing (normal) training data, whereas the second encodes low-level
residual image characteristics. To ensure the dual representations encode
mutually exclusive information, a disentanglement procedure is designed around
a latent (proxy) classifier. Additionally, a novel consistency loss is proposed
to prevent information leakage between the latent spaces. The model is trained
in a one-class learning setting using normal training data only. Due to the
separation of semantically-relevant and residual information, Y-GAN is able to
derive informative data representations that allow for efficient anomaly
detection across a diverse set of anomaly detection tasks. The model is
evaluated in comprehensive experiments with several recent anomaly detection
models using four popular datasets, i.e., MNIST, FMNIST and CIFAR10, and
PlantVillage.
