Anomaly detection is a significant and hence well-studied problem. However,
developing effective anomaly detection methods for complex and high-dimensional
data remains a challenge. As Generative Adversarial Networks (GANs) are able to
model the complex high-dimensional distributions of real-world data, they offer
a promising approach to address this challenge. In this work, we propose an
anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based
on bi-directional GANs, that derives adversarially learned features for the
anomaly detection task. ALAD then uses reconstruction errors based on these
adversarially learned features to determine if a data sample is anomalous. ALAD
builds on recent advances to ensure data-space and latent-space
cycle-consistencies and stabilize GAN training, which results in significantly
improved anomaly detection performance. ALAD achieves state-of-the-art
performance on a range of image and tabular datasets while being several
hundred-fold faster at test time than the only published GAN-based method.

本文提出一种基于双向 GAN 的异常检测方法 ALAD，通过对偶学习特征并利用这些特征的重构误差来确定数据样本是否异常，从而在一系列图像和表格数据集上取得了最先进的性能。