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The original was posted on /r/machinelearning by /u/LostSleepyDreamer on 2024-10-30 14:23:45+00:00.

Hi everyone,

I’m currently interested in exploring generative models defined over Riemannian manifolds. Though the idea is theoretically appealing, I have trouble understanding the practical motivation behind this approach, and whether any useful/large scale model has been developed lately based on it.

To be more precise, I am looking at the following set of papers.

Generalizing diffusion models to the Riemannian setting :

Riemannian Diffusion Models, Riemannian Score-Based Generative Modelling

Scaling these models:

Scaling Riemannian Diffusion Models

I don’t understand how impactful the experimental results really are, and what the interest for these models are whether in the industry or in the research community. 

If anyone has any thoughts about the interrogations I have, I’d be happy to start a discussion here. I’d be extremely grateful for your insights! Thanks for any help