Anomaly Detection with Adversarial Dual Autoencoders
This work addresses instability in GAN-based anomaly detection, which is a problem for applications like medical imaging, though it appears incremental as it builds on prior GAN methods.
The paper tackles the challenge of unstable GAN training in anomaly detection by introducing Adversarial Dual Autoencoders (ADAE), a framework using two autoencoders as generator and discriminator, and reports strong experimental evidence across datasets including brain tumor detection.
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.