Latest revision as of 00:24, 20 January 2026

Diffusion models

A class of latent variable generative models consisting of three major components: a forward process, a reverse process, and a sampling procedure. The goal of the diffusion model is to learn a diffusion process that generates the probability distribution of a given dataset. It is widely used in computer vision on a variety of tasks, including image denoising, inpainting, super-resolution, and image generation.

Source: NIST AI 100-2e2025 | Category:

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