Summary of A Survey on Diffusion Models For Inverse Problems, by Giannis Daras et al.
A Survey on Diffusion Models for Inverse Problems
by Giannis Daras, Hyungjin Chung, Chieh-Hsin Lai, Yuki Mitsufuji, Jong Chul Ye, Peyman Milanfar, Alexandros G. Dimakis, Mauricio Delbracio
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 survey paper provides an overview of methods that utilize pre-trained diffusion models to solve inverse problems without requiring further training. The authors introduce taxonomies to categorize these methods based on both the problems they address and the techniques they employ. The paper analyzes connections between different approaches, offering insights into their practical implementation and highlighting important considerations. Specifically, it discusses challenges and potential solutions associated with using latent diffusion models for inverse problems in image restoration and reconstruction. The authors’ work aims to be a valuable resource for those interested in learning about the intersection of diffusion models and inverse problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computer programs called “diffusion models” can help solve puzzles, like fixing damaged pictures or reconstructing old images. These models are really good at making new images that look similar to ones they’ve seen before. The paper shows how these models can be used to fix problems without needing more training. It also talks about different ways to use these models and some challenges that come with it. |
Keywords
» Artificial intelligence » Diffusion
