Summary of Diffusion Model From Scratch, by Wang Zhen and Dong Yunyun
Diffusion Model from Scratch
by Wang Zhen, Dong Yunyun
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper aims to provide a foundational understanding of generative models by tracing their evolution from VAEs to DDPM through mathematical derivations and an analytical approach. The authors explore the core ideas and improvement strategies of mainstream methodologies, offering guidance for undergraduate and graduate students interested in learning about diffusion models. Specifically, they examine the Denoising Diffusion Probability Model (DDPM), a complex generative model that has become the most popular type of generative model. By providing detailed mathematical derivations and a problem-oriented approach, this paper helps readers build a comprehensive understanding of these models and their underlying processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for students to learn about diffusion generative models by showing how they evolved from other types of models. It explains the important ideas and ways to improve current methods in detail, using math and problem-solving approaches. The goal is to help undergraduate and graduate students understand these complex models and how they work. |
Keywords
» Artificial intelligence » Diffusion » Generative model » Probability
