Summary of Improving Decoupled Posterior Sampling For Inverse Problems Using Data Consistency Constraint, by Zhi Qi et al.
Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint
by Zhi Qi, Shihong Yuan, Yuyin Yuan, Linling Kuang, Yoshiyuki Kabashima, Xiangming Meng
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 proposes a new method called Guided Decoupled Posterior Sampling (GDPS) to improve the performance of diffusion models in solving inverse problems. The proposed method integrates a data consistency constraint into the reverse process to facilitate a smoother transition and more effective convergence towards the target distribution. GDPS is evaluated on various datasets, including FFHQ and ImageNet, across different tasks and conditions, demonstrating state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GDPS helps diffusion models solve inverse problems by ensuring a smooth transition during optimization. This method uses data consistency to improve accuracy and is tested on popular datasets like FFHQ and ImageNet. |
Keywords
» Artificial intelligence » Diffusion » Optimization
