GANs have two competing modules: the generator module is trained to generate new examples, and the discriminator module is trained to discriminate real examples from generated examples. The training procedure of GAN is modeled as a finitely repeated simultaneous game. Each module tries to increase its performance at every repetition of the base game (at every batch of training data) in a non-cooperative manner. We observed that each module can perform better and learn faster if training is modeled as an infinitely repeated simultaneous game. At every repetition of the base game (at every batch of training data) the stronger module (whose performance is increased or remains the same compared to the previous batch of training data) cooperates with the weaker module (whose performance is decreased compared to the previous batch of training data) and only the weaker module is allowed to increase its performance.
翻译:GAN 有两个相互竞争的模块: 生成模块受过培训以生成新的实例, 歧视模块受过培训以区别从生成的实例中得出的真实实例。 GAN 的培训程序被建模成一个有限的重复同步游戏。 每个模块都试图以不合作的方式提高基础游戏每重复一次( 每批培训数据)的性能。 我们观察到, 如果将培训建模成一个无限重复的同步游戏, 每个模块可以更好和更快地进行学习。 在每重复一次基础游戏( 每批培训数据)时,强的模块(其性能增加或保持与前一批培训数据相同)与较弱的模块合作(其性能比前一批培训数据减少), 只有较弱的模块可以提高性能。