Diffusion Family: Score Matching Langevin Dynamics and More
References: Tutorial on Diffusion Models for Imaging and Vision by Stanley Chan Dr. Yang Song’s blog on Score Matching Basics of Langevin Dynamics Unlike DDPM, which models the generative model as a hidden variable model with $x_{1:T}$ as the hidden variables, score-matching models, while deeply linked to DDPM, starts from a sampling view and later concerns about the distribution we sampled from. Let’s start with an assumption that we have a distribution $p(x)$ that we can sample from, and this distribution is exactly the distribution we want (say, the image distribution of a cat)....