Forward-Backward Generative Adversarial Networks for Anomaly Detection

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Youngnam Kim, Seungjin Choi ;
Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:1142-1155, 2019.

Abstract

Generative adversarial network (GAN) has established itself as a promising model for density estimation, with its wide applications to various problems. Of particular interest in this paper is the problem of {\em anomaly detection} which involves identifying events that do not conform to expected patterns in data. Recent application of GANs to the task of anomaly detection, resort to their ability for learning probability distributions of normal examples, so that abnormal examples or outliers are detected when they reside in very low-probability regimes. Existing GAN methods often suffer from the bad {\em cycle-consistency} problem, which yields the large reconstruction error so that the anomaly detection performance is degraded. In order to alleviate this, we present a model that consists of a forward GAN and backward GAN, each of which has an individual discriminator, that are coupled by enforcing feature matching in two discriminators. We show that our forward-backward GANs (FBGANs) better captures the data distribution so that the anomaly detection performance is improved over existing GAN-based methods. Experiments on MNIST an KDD99 datasets demonstrate that our method, FBGANs, outperforms existing state-of-the-art anomaly detection methods, in terms of the area under precision recall curve (AUPR) and $F_{1}$-score.

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