Adversarially Learned Anomaly Detection
This addresses the challenge of effective anomaly detection for high-dimensional data, offering a faster and more accurate solution, though it is incremental as it builds on existing GAN advances.
The paper tackled anomaly detection in complex, high-dimensional data by proposing ALAD, a method based on bi-directional GANs that uses reconstruction errors from adversarially learned features, achieving state-of-the-art performance on image and tabular datasets and being several hundred-fold faster at test time than existing GAN-based methods.
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.