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.

介绍了一种基于 GAN 的异常检测框架 - Adversarial Dual Autoencoders (ADAE)，由两个自编码器作为生成器和判别器以提高训练稳定性，利用判别器重建误差作为异常得分以获得更好的检测性能。在不同复杂度的数据集上进行的实验表明，该模型具有良好的鲁棒性，可用于不同场景中，其中之一是脑肿瘤检测。