Web Reference: Jul 11, 2021 · Diffusion models are inspired by non-equilibrium thermodynamics. They define a Markov chain of diffusion steps to slowly add random noise to data and then learn to reverse the diffusion process to construct desired data samples from the noise. 3 days ago · Diffusion models are generative models that create realistic data by learning to remove noise from random inputs. During training, noise is gradually added to real data so the model learns how data degrades. In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of two major components: the forward diffusion process, and the reverse sampling process.
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