  In terms of Generative Adversarial Networks (GANs), the information metric to
discriminate the generative data from the real data, lies in the key point of
generation efficiency, which plays an important role in GAN-based applications,
especially in anomaly detection. As for the original GAN, there exist drawbacks
for its hidden information measure based on KL divergence on rare events
generation and training performance for adversarial networks. Therefore, it is
significant to investigate the metrics used in GANs to improve the generation
ability as well as bring gains in the training process. In this paper, we adopt
the exponential form, referred from the information measure, i.e. MIM, to
replace the logarithm form of the original GAN. This approach is called
MIM-based GAN, has better performance on networks training and rare events
generation. Specifically, we first discuss the characteristics of training
process in this approach. Moreover, we also analyze its advantages on
generating rare events in theory. In addition, we do simulations on the
datasets of MNIST and ODDS to see that the MIM-based GAN achieves
state-of-the-art performance on anomaly detection compared with some classical
GANs.
