Generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as a two-player zero-sum game between two neural networks. Here, we leverage this game theoretic view to study the convergence behavior of the training process. Inspired by the fictitious play learning process, a novel training method, referred to as Fictitious GAN, is introduced. Fictitious GAN trains the deep neural networks using a mixture of historical models. Specifically, the discriminator (resp. generator) is updated according to the best-response to the mixture outputs from a sequence of previously trained generators (resp. discriminators). It is shown that Fictitious GAN can effectively resolve some convergence issues that cannot be resolved by the standard training approach. It is proved that asymptotically the average of the generator outputs has the same distribution as the data samples.

翻译：产生对抗性网络(GANs)是学习基因模型的有力工具,在实践中,培训可能缺乏趋同性。GANs通常被视为两个神经网络之间的双玩零和游戏。在这里,我们利用这个游戏理论视角研究培训过程的趋同行为。在虚构的游戏学习过程的启发下,引入了一种称为Fictititious GAN的新型培训方法。Fictititious GAN利用各种历史模型混合对深层神经网络进行培训。具体来说,歧视者(再生发电机)是根据以前训练的发电机序列(再生导体)对混合输出的最佳反应来更新的。这表明,Fictititious GAN可以有效地解决一些无法通过标准培训方法解决的趋同性问题。事实证明,发电机产出的平均值与数据样本一样,具有同样的分布。