  Identifying anomalies refers to detecting samples that do not resemble the
training data distribution. Many generative models have been used to find
anomalies, and among them, generative adversarial network (GAN)-based
approaches are currently very popular. GANs mainly rely on the rich contextual
information of these models to identify the actual training distribution.
Following this analogy, we suggested a new unsupervised model based on GANs --a
combination of an autoencoder and a GAN. Further, a new scoring function was
introduced to target anomalies where a linear combination of the internal
representation of the discriminator and the generator's visual representation,
plus the encoded representation of the autoencoder, come together to define the
proposed anomaly score. The model was further evaluated on benchmark datasets
such as SVHN, CIFAR10, and MNIST, as well as a public medical dataset of
leukemia images. In all the experiments, our model outperformed its existing
counterparts while slightly improving the inference time.
