Summary of G2d2: Gradient-guided Discrete Diffusion For Image Inverse Problem Solving, by Naoki Murata et al.
G2D2: Gradient-guided Discrete Diffusion for image inverse problem solving
by Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Bac Nguyen, Stefano Ermon, Yuki Mitsufuji
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 method for addressing linear inverse problems by leveraging image-generation models based on discrete diffusion as priors. The authors build upon recent literature that has successfully utilized continuous variables as priors, but extend this approach to handle discrete and non-differentiable models. By approximating the true posterior distribution with a variational distribution constructed from categorical distributions and continuous relaxation techniques, the method overcomes the limitations of traditional discrete diffusion models. Furthermore, the authors employ a star-shaped noise process to mitigate drawbacks of these models. The results demonstrate that this approach performs comparably to continuous diffusion techniques. This is the first approach to use discrete diffusion model-based priors for solving image inverse problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us solve tricky math problems by using special computer programs that create images. These programs are based on a new way of thinking about how computers can learn from pictures. The old way was only good for creating continuous, flowing images, but this new approach is better at making discrete, pixelated images. To make it work, the authors had to find a way to combine two different types of computer code: one that works well with numbers and another that works well with words or symbols. They also added some extra tricks to make sure the program didn’t get stuck or lose its way. The results are impressive, showing that this new approach can solve problems just as well as the old way. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Image generation
