这些GAN方法仅使用一个判别器对数据分布进行建模，作为一个损失函数通过

These methods use only a GAN discriminator distributed data model, as a function of loss, learning signal generator to provide a process by confrontation between the generator and the discriminator to generate real samples.

These GAN methods use only one differential to model the data distribution, and as a loss function, a process of confrontation between the generator and the other, provides a learning signal for the generator to generate a real sample.

These Gan methods use only one discriminator to model the data distribution. As a loss function, they provide a learning signal for the generator to generate real samples through the confrontation process between the generator and the discriminator.