Generative adversarial networks (GANs) are challenging to train stably, and a promising remedy of injecting instance noise into the discriminator input has not been very effective in practice. In this paper, we propose Diffusion-GAN, a novel GAN framework that leverages a forward diffusion chain to generate Gaussian-mixture distributed instance noise. Diffusion-GAN consists of three components, including an adaptive diffusion process, a diffusion timestep-dependent discriminator, and a generator. Both the observed and generated data are diffused by the same adaptive diffusion process. At each diffusion timestep, there is a different noise-to-data ratio and the timestep-dependent discriminator learns to distinguish the diffused real data from the diffused generated data. The generator learns from the discriminator's feedback by backpropagating through the forward diffusion chain, whose length is adaptively adjusted to balance the noise and data levels. We theoretically show that the discriminator's timestep-dependent strategy gives consistent and helpful guidance to the generator, enabling it to match the true data distribution. We demonstrate the advantages of Diffusion-GAN over strong GAN baselines on various datasets, showing that it can produce more realistic images with higher stability and data efficiency than state-of-the-art GANs.

翻译：生成对抗网络（GAN）在稳定训练方面面临挑战，而向判别器输入注入实例噪声这一有前景的补救措施在实践中效果并不理想。本文提出Diffusion-GAN，一种新颖的GAN框架，它利用前向扩散链生成高斯混合分布的实例噪声。Diffusion-GAN包含三个组成部分：自适应扩散过程、依赖扩散时间步的判别器以及生成器。观测数据和生成数据均由相同的自适应扩散过程进行扩散。在每个扩散时间步中，噪声与数据的比例不同，依赖时间步的判别器学习区分扩散后的真实数据与扩散后的生成数据。生成器通过前向扩散链反向传播，从判别器的反馈中学习，该链的长度被自适应调整以平衡噪声与数据水平。我们从理论上证明，判别器依赖时间步的策略能为生成器提供一致且有益的指导，使其能够匹配真实数据分布。我们在多个数据集上展示了Diffusion-GAN相较于强GAN基线模型的优势，表明它能生成比现有最先进GAN更逼真的图像，同时具有更高的稳定性和数据效率。