  When formulated as an unsupervised learning problem, anomaly detection often
requires a model to learn the distribution of normal data. Previous works apply
Generative Adversarial Networks (GANs) to anomaly detection tasks and show good
performances from these models. Motivated by the observation that GAN ensembles
often outperform single GANs in generation tasks, we propose to construct GAN
ensembles for anomaly detection. In the proposed method, a group of generators
and a group of discriminators are trained together, so every generator gets
feedback from multiple discriminators, and vice versa. Compared to a single
GAN, a GAN ensemble can better model the distribution of normal data and thus
better detect anomalies. Our theoretical analysis of GANs and GAN ensembles
explains the role of a GAN discriminator in anomaly detection. In the empirical
study, we evaluate ensembles constructed from four types of base models, and
the results show that these ensembles clearly outperform single models in a
series of tasks of anomaly detection.

Machine Learning;Artificial Intelligence