For stable training of generative adversarial networks (GANs), injecting instance noise into the input of the discriminator is considered as a theoretically sound solution, which, however, has not yet delivered on its promise in practice. This paper introduces Diffusion-GAN that employs a Gaussian mixture distribution, defined over all the diffusion steps of a forward diffusion chain, to inject instance noise. A random sample from the mixture, which is diffused from an observed or generated data, is fed as the input to the discriminator. The generator is updated by backpropagating its gradient through the forward diffusion chain, whose length is adaptively adjusted to control the maximum noise-to-data ratio allowed at each training step. Theoretical analysis verifies the soundness of the proposed Diffusion-GAN, which provides model- and domain-agnostic differentiable augmentation. A rich set of experiments on diverse datasets show that Diffusion-GAN can provide stable and data-efficient GAN training, bringing consistent performance improvement over strong GAN baselines for synthesizing photo-realistic images.

翻译：为了对基因对抗网络进行稳定的培训,将实例噪音注入歧视者的投入中被认为是一种理论上合理的解决办法,但实际上还没有实现这一办法。本文介绍Difmissian混合物分布的DifmissianGAN,该混合物在前方扩散链的所有扩散步骤中都有定义,进入试验噪音。该混合物的随机样本从观测到或生成的数据中传播出来,作为向歧视者输入的输入材料。该生成器通过前方扩散链对其梯度进行反射更新,前方扩散链的长度正在适应性调整,以控制每一培训步骤允许的最大噪声对数据比率。理论分析核实了拟议的Difmulpulation-GAN的可靠性,它提供了模型和领域性异性增强。关于多种数据集的丰富实验表明,Difmulation-GAN能够提供稳定和数据效率高的GAN培训,在合成摄影现实图像的强大GAN基线上实现连续的性改进性能。