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Found 4 papers, 4 papers with code
DeFoG: Discrete Flow Matching for Graph Generation
We also theoretically demonstrate that, for general discrete data, discrete flow models can faithfully replicate the ground truth distribution - a result that naturally extends to graph data and reinforces DeFoG's foundations.
Diff-BGM: A Diffusion Model for Video Background Music Generation
When editing a video, a piece of attractive background music is indispensable.
Sparse Training of Discrete Diffusion Models for Graph Generation
Generative graph models struggle to scale due to the need to predict the existence or type of edges between all node pairs.
Class-Balancing Diffusion Models
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.
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