Adversarially Learned Anomaly Detection

Abstract--Anomaly detection is a significant and hence wellstudied 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 bidirectional 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 hundredfold faster at test time than the only published GAN-based method. I. INTRODUCTION Anomaly detection is a problem of great practical significance acrossa range of real-world settings, including cyber-security [1], manufacturing [2], fraud detection, and medical imaging [3].