Summary of Otlrm: Orthogonal Learning-based Low-rank Metric For Multi-dimensional Inverse Problems, by Xiangming Wang et al.
OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse Problems
by Xiangming Wang, Haijin Zeng, Jiaoyang Chen, Sheng Liu, Yongyong Chen, Guoqing Chao
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The paper introduces a novel data-driven generative low-rank tensor singular value decomposition (t-SVD) model based on a learnable orthogonal transform, which can be naturally solved under its representation. The proposed model leverages the linear algebra theorem of the Householder transformation to construct an endogenously orthogonal matrix adaptable to neural networks, optimizing it as arbitrary orthogonal matrices. Additionally, the paper proposes a low-rank solver that utilizes an efficient representation of generative networks to obtain low-rank structures. The authors demonstrate the effectiveness of their approach through extensive experiments, highlighting significant restoration enhancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to analyze complex data like images and videos by using something called tensor singular value decomposition (t-SVD). This helps solve problems like filling in missing pieces of information or removing noise from images. The existing methods for doing this rely on pre-designed transformations, which can be inflexible. The authors propose a new approach that uses a “learnable” orthogonal transform to adapt to different situations. They also suggest a way to make their method work with deep neural networks. The results show that their approach is better at restoring the original information than existing methods. |
