✳flâneur — a map of the web's best reading

Diffusion model - Wikipedia

en.wikipedia.org · 15,436 words · saved by 1 readers

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. The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data.[1] A trained diffusion model can be sampled in many ways, with different efficiency and quality. There are various equivalent formalisms, including Markov chains, denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations.[2] They are typically trained using variational inference.[3] The model responsible for

Diffusion model - Wikipedia Jump to content From Wikipedia, the free encyclopedia Technique for the generative modeling of a continuous probability distribution This article is about the technique in generative statistical modeling. For other uses, see Diffusion (disambiguation) . This article discusses diffusion modeling of a continuous distribution. For the modeling of a discrete distribution, see Discrete diffusion model . Part of a series on Machine learning and data mining Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement l

Explore this link on the map →

saved by

related reading