  Semi-supervised and unsupervised Generative Adversarial Networks (GAN)-based
methods have been gaining popularity in anomaly detection task recently.
However, GAN training is somewhat challenging and unstable. Inspired from
previous work in GAN-based image generation, we introduce a GAN-based anomaly
detection framework - Adversarial Dual Autoencoders (ADAE) - consists of two
autoencoders as generator and discriminator to increase training stability. We
also employ discriminator reconstruction error as anomaly score for better
detection performance. Experiments across different datasets of varying
complexity show strong evidence of a robust model that can be used in different
scenarios, one of which is brain tumor detection.
