Diffusion Model from Scratch

Fuente: arXiv
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Autores principales: Zhen, Wang, Yunyun, Dong
Formato: Preprint
Publicado: 2024
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author Zhen, Wang
Yunyun, Dong
author_facet Zhen, Wang
Yunyun, Dong
contents Diffusion generative models are currently the most popular generative models. However, their underlying modeling process is quite complex, and starting directly with the seminal paper Denoising Diffusion Probability Model (DDPM) can be challenging. This paper aims to assist readers in building a foundational understanding of generative models by tracing the evolution from VAEs to DDPM through detailed mathematical derivations and a problem-oriented analytical approach. It also explores the core ideas and improvement strategies of current mainstream methodologies, providing guidance for undergraduate and graduate students interested in learning about diffusion models.
format Preprint
id arxiv_https___arxiv_org_abs_2412_10824
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Diffusion Model from Scratch
Zhen, Wang
Yunyun, Dong
Computer Vision and Pattern Recognition
Machine Learning
Diffusion generative models are currently the most popular generative models. However, their underlying modeling process is quite complex, and starting directly with the seminal paper Denoising Diffusion Probability Model (DDPM) can be challenging. This paper aims to assist readers in building a foundational understanding of generative models by tracing the evolution from VAEs to DDPM through detailed mathematical derivations and a problem-oriented analytical approach. It also explores the core ideas and improvement strategies of current mainstream methodologies, providing guidance for undergraduate and graduate students interested in learning about diffusion models.
title Diffusion Model from Scratch
topic Computer Vision and Pattern Recognition
Machine Learning
url https://arxiv.org/abs/2412.10824