Summary of Solving Inverse Problems with Model Mismatch Using Untrained Neural Networks Within Model-based Architectures, by Peimeng Guan et al.
Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures
by Peimeng Guan, Naveed Iqbal, Mark A. Davenport, Mudassir Masood
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 method combines model-based deep learning techniques, specifically loop unrolling (LU) and deep equilibrium models (DEQ), with an untrained forward model residual block to address forward model mismatch in inverse problems. The approach enables simultaneous fitting of the forward model and reconstruction in a single pass, without requiring additional data. The authors demonstrate significant quality improvements in removing artifacts and preserving details across three distinct applications, including linear and nonlinear inverse problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines special machine learning techniques to solve tricky math problems. They use a type of deep learning called “model-based” that’s really good at solving certain kinds of puzzles. To make it even better, they add a special trick that helps the model not get fooled by mistakes in the information it starts with. This makes it much more accurate and reliable. The results show that their method can solve problems faster and better than before, and it works for both easy and hard math problems. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
