Diffusion models, a type of generative model, have demonstrated great potential for synthesizing highly detailed images. By integrating with GAN, advanced diffusion models like DDGAN \citep{xiao2022DDGAN} could approach real-time performance for expansive practical applications. While DDGAN has effectively addressed the challenges of generative modeling, namely producing high-quality samples, covering different data modes, and achieving faster sampling, it remains susceptible to performance drops caused by datasets that are corrupted with outlier samples. This work introduces a robust training technique based on semi-unbalanced optimal transport to mitigate the impact of outliers effectively. Through comprehensive evaluations, we demonstrate that our robust diffusion GAN (RDGAN) outperforms vanilla DDGAN in terms of the aforementioned generative modeling criteria, i.e., image quality, mode coverage of distribution, and inference speed, and exhibits improved robustness when dealing with both clean and corrupted datasets.

通过半平衡最优传输的鲁棒训练技术，本文介绍了一种能有效缓解偏离值样本影响的鲁棒扩散生成对抗网络（RDGAN），通过全面评估展示了RDGAN在图像质量、分布模式覆盖以及推理速度等生成建模标准上优于标准DDGAN，且在处理干净和损坏数据集时表现出改进的鲁棒性。

鲁棒扩散GAN基于半不平衡最优传输