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

Robust Diffusion GAN using Semi-Unbalanced Optimal Transport

Despite the proliferation of generative models, achieving fast sampling
during inference without compromising sample diversity and quality remains
challenging. Existing models such as Denoising Diffusion Probabilistic Models
(DDPM) deliver high-quality, diverse samples but are slowed by an inherently
high number of iterative steps. The Denoising Diffusion Generative Adversarial
Networks (DDGAN) attempted to circumvent this limitation by integrating a GAN
model for larger jumps in the diffusion process. However, DDGAN encountered
scalability limitations when applied to large datasets. To address these
limitations, we introduce a novel approach that tackles the problem by matching
implicit and explicit factors. More specifically, our approach involves
utilizing an implicit model to match the marginal distributions of noisy data
and the explicit conditional distribution of the forward diffusion. This
combination allows us to effectively match the joint denoising distributions.
Unlike DDPM but similar to DDGAN, we do not enforce a parametric distribution
for the reverse step, enabling us to take large steps during inference. Similar
to the DDPM but unlike DDGAN, we take advantage of the exact form of the
diffusion process. We demonstrate that our proposed method obtains comparable
generative performance to diffusion-based models and vastly superior results to
models with a small number of sampling steps.

通过匹配隐式和显式因素，我们提出了一种新的方法，以解决生成模型中的采样难题，该方法利用隐式模型匹配噪声数据的边缘分布和前向扩散的显式条件分布，以有效地匹配联合降噪分布，并获得与扩散模型相当的生成性能和比采样步骤少的模型相比更好的结果。