Summary of Random Walks with Tweedie: a Unified Framework For Diffusion Models, by Chicago Y. Park et al.
Random Walks with Tweedie: A Unified Framework for Diffusion Models
by Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona, Brendt Wohlberg, Ulugbek S. Kamilov
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| 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 presents a novel approach for designing generative diffusion model algorithms based on an interpretation of diffusion sampling as random walks. Score-based diffusion models have achieved state-of-the-art results in image generation and inverse problems, but the underlying theory is complex. The authors provide a simple, self-contained theoretical justification for score-based-diffusion models using Tweedie’s formula and random walks, avoiding Markov chains or reverse diffusion. This framework leads to unified algorithmic templates for network training and sampling, cleanly separating training from sampling. The proposed approach enables conditional sampling without likelihood approximation and has the potential to improve existing diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special kind of computer program that can create new images or solve complex problems by making small changes to previous results. This paper shows how to make these programs, called generative diffusion models, work better by understanding the way they change things. The authors use a simple idea about random movements to explain why some programs are more effective than others. They also show that this approach can help create new images or solve problems in a specific order, without needing to know how the program works. Overall, this paper helps us understand and improve these powerful computer tools. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Image generation » Likelihood
