Summary of Think Twice Before You Act: Improving Inverse Problem Solving with Mcmc, by Yaxuan Zhu et al.
Think Twice Before You Act: Improving Inverse Problem Solving With MCMC
by Yaxuan Zhu, Zehao Dou, Haoxin Zheng, Yasi Zhang, Ying Nian Wu, Ruiqi Gao
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 proposes a novel inference algorithm called Diffusion Posterior MC (DPMC) to solve inverse problems using pre-trained diffusion models. The approach builds upon the strengths of Diffusion Posterior Sampling (DPS), which approximates the posterior distribution of data given the measure using Tweedie’s formula. However, DPS can be inaccurate for high noise levels, hindering its performance. To address this limitation, DPMC uses Annealed MCMC to reduce accumulated error by encouraging samples to follow intermediate distributions at decreasing noise levels. The algorithm is tested on various inverse problems, including super resolution, Gaussian deblurring, motion deblurring, inpainting, and phase retrieval. Experimental results show that DPMC outperforms DPS with fewer evaluations across most tasks, making it a competitive approach in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to solve puzzles using computers. The puzzle is called an “inverse problem” and it’s like trying to find the original picture from a blurry one. Right now, there’s an algorithm called DPS that does this well, but it gets stuck when there’s too much noise in the picture. To fix this, the researchers created a new algorithm called DPMC that uses a special method called Annealed MCMC. This helps reduce mistakes by making the computer try different solutions at lower and lower levels of noise. The new algorithm was tested on many different types of puzzles and it did better than DPS in most cases. |
Keywords
» Artificial intelligence » Diffusion » Inference » Super resolution
