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.
翻译:在基因反转网络(GANs)方面,区别实际数据基因数据的信息衡量标准在于产生效率的关键点,在以GAN为基础的应用中,特别是在异常现象检测中发挥重要作用。关于原GAN,基于KL在罕见事件生成和培训性能方面存在差异的隐藏信息衡量方法存在缺陷,因此,必须调查GANs中用来提高生成能力和在培训过程中取得收益的衡量标准。在本文中,我们采用了从信息计量(即MIM)中引用的指数形式,以取代原GAN的对数形式。这种方法被称为MIM-GAN,在网络培训和罕见事件生成方面表现更好。具体地说,我们首先讨论这一方法的培训过程的特点。此外,我们还分析了其在理论中产生罕见事件的好处。此外,我们模拟了MNIST和ODDDDDS的数据集,以便看到以MIM为基础的GAN为基地的GAN在GAN的古典检测中取得了某些古典的GAN-AN性能。