Summary of Diffusion State-guided Projected Gradient For Inverse Problems, by Rayhan Zirvi et al.
Diffusion State-Guided Projected Gradient for Inverse Problems
by Rayhan Zirvi, Bahareh Tolooshams, Anima Anandkumar
First submitted to arxiv on: 4 Oct 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 In this paper, the authors propose a novel method called Diffusion State-Guided Projected Gradient (DiffStateGrad) to improve the performance and robustness of diffusion models in solving inverse problems. The method projects the measurement gradient onto a subspace that is a low-rank approximation of an intermediate state of the diffusion process. This allows for better preservation of the diffusion process on the prior manifold, reducing artifacts in applications such as image restoration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This technique can be used to improve the robustness of diffusion models in terms of the choice of measurement guidance step size and noise, while also improving their worst-case performance. The authors demonstrate that DiffStateGrad improves upon the state-of-the-art on linear and nonlinear image restoration inverse problems. |
Keywords
» Artificial intelligence » Diffusion
