  Anomaly detection is a fundamental problem in computer vision area with many
real-world applications. Given a wide range of images belonging to the normal
class, emerging from some distribution, the objective of this task is to
construct the model to detect out-of-distribution images belonging to abnormal
instances. Semi-supervised Generative Adversarial Networks (GAN)-based methods
have been gaining popularity in anomaly detection task recently. However, the
training process of GAN is still unstable and challenging. To solve these
issues, a novel adversarial dual autoencoder network is proposed, in which the
underlying structure of training data is not only captured in latent feature
space, but also can be further restricted in the space of latent representation
in a discriminant manner, leading to a more accurate detector. In addition, the
auxiliary autoencoder regarded as a discriminator could obtain an more stable
training process. Experiments show that our model achieves the state-of-the-art
results on MNIST and CIFAR10 datasets as well as GTSRB stop signs dataset.
