Anomaly Detection with Adversarial Dual Autoencoders

Understand

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.