Sparse Training of Discrete Diffusion Models for Graph Generation

1 code implementation • 3 Nov 2023 • Yiming Qin, Clement Vignac, Pascal Frossard

In this work, we introduce SparseDiff, a denoising diffusion model for graph generation that is able to exploit sparsity during its training phase.

Denoising Graph Generation

Class-Balancing Diffusion Models

1 code implementation • CVPR 2023 • Yiming Qin, Huangjie Zheng, Jiangchao Yao, Mingyuan Zhou, Ya zhang

To tackle this problem, we set from the hypothesis that the data distribution is not class-balanced, and propose Class-Balancing Diffusion Models (CBDM) that are trained with a distribution adjustment regularizer as a solution.