Robust Diffusion GAN using Semi-Unbalanced Optimal Transport
Quan Dao Binh Ta Tung Pham Anh Tran
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.