论文标题
设计gan：可能比率方法
Designing GANs: A Likelihood Ratio Approach
论文作者
论文摘要
我们对生成网络的设计感兴趣。这些数学结构的训练主要是在对抗（Min-Max）优化问题的帮助下进行的。我们提出了一种简单的方法来构建相应解决方案的一致性，以确保确保一致性。我们给出了由我们的方法开发的特征示例，其中一些可以从其他应用程序中识别出来，其中一些是第一次介绍。我们提出了一个新的指标，即可能性比率，可以在线使用，以检查不同生成对抗网络（GAN）期间的收敛性和稳定性。最后，我们通过使用不同配置和尺寸的神经网络将它们应用于众所周知的数据集来比较各种可能性。
We are interested in the design of generative networks. The training of these mathematical structures is mostly performed with the help of adversarial (min-max) optimization problems. We propose a simple methodology for constructing such problems assuring, at the same time, consistency of the corresponding solution. We give characteristic examples developed by our method, some of which can be recognized from other applications, and some are introduced here for the first time. We present a new metric, the likelihood ratio, that can be employed online to examine the convergence and stability during the training of different Generative Adversarial Networks (GANs). Finally, we compare various possibilities by applying them to well-known datasets using neural networks of different configurations and sizes.
