Neural denoising diffusion models

Denoising diffusion probabilistic models (DDPMs), score-based generative models, generative diffusion processes, neural energy models…


AFAICS, generative models using score-matching to learn and Langevin MCMC to sample. There are various tricks needed to to do it with successive denoising steps and interpretation in terms of SDEs. I am vaguely aware that this oversimplifies a rich and interesting history of convergence of many useful techniques, but not invested enough to perform a reconstruction upon the details.

Training: score matching

Denoising score matching Hyvärinen (2005). See score matching or McAllester (2023) for an introduction.

Sampling: Langevin dynamics

See Langevin samplers.

Image generation in particular

See image generation with diffusion.


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