Summary of Fast Samplers For Inverse Problems in Iterative Refinement Models, by Kushagra Pandey et al.
Fast Samplers for Inverse Problems in Iterative Refinement Models
by Kushagra Pandey, Ruihan Yang, Stephan Mandt
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 The proposed framework constructs efficient samplers for inverse problems, such as super-resolution, inpainting, or deblurring, using pre-trained diffusion or flow-matching models. The Conditional Conjugate Integrators project the conditional dynamics into a more amenable space for sampling, leveraging the specific form of the inverse problem. This plug-and-play framework complements popular posterior approximation methods and is evaluated on various linear image restoration tasks across multiple datasets. The method outperforms competing baselines, requiring as few as 5 conditional sampling steps to generate high-quality samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to make a really good picture from a blurry one. Or take a small part of an old photo and make it look like the whole thing again. This is called an “inverse problem.” Right now, computers need to do many calculations (or “steps”) to make these kinds of images. But what if they could do it in just a few steps? That’s what this paper is all about. It finds a way to use special models that already know how to make good pictures from bad ones and apply them to solve these inverse problems quickly. |
Keywords
» Artificial intelligence » Diffusion » Super resolution
