Abstract
Anomaly detection has been of practical interest to many fields of engineering. Generative Adversarial Networks (GAN), which have excellent performance in modelling the complex distributions of real-world data, shows its effectiveness in anomaly detection. In this paper, the application of Bidirectional Generative Adversarial Network (BiGAN) model for anomaly detection has been introduced and investigated. Besides, scoring functions has also been applied to estimate the anomaly of new data samples. The proposed Bidirectional Generative Adversarial Network based anomaly detection method demonstrates a competitive performance on Tennessee Eastman (TE) datasets, while being several hundred-fold faster at test time than other published GAN-based method.
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