  Generative adversarial networks (GANs) have shown promise for various
problems including anomaly detection. When anomaly detection is performed using
GAN models that learn only the features of normal data samples, data that are
not similar to normal data are detected as abnormal samples. The present
approach is developed by employing a dual-encoder in a bidirectional GAN
architecture that is trained simultaneously with a generator and a
discriminator network. Through the learning mechanism, the proposed method aims
to reduce the problem of bad cycle consistency, in which a bidirectional GAN
might not be able to reproduce samples with a large difference between normal
and abnormal samples. We assume that bad cycle consistency occurs when the
method does not preserve enough information of the sample data. We show that
our proposed method performs well in capturing the distribution of normal
samples, thereby improving anomaly detection on GAN-based models. Experiments
are reported in which our method is applied to publicly available datasets,
including application to a brain magnetic resonance imaging anomaly detection
system.
