What is Wrong with End-to-End Learning for Phase Retrieval?
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914725840814080 |
|---|---|
| author | Zhang, Wenjie Wan, Yuxiang Zhuang, Zhong Sun, Ju |
| author_facet | Zhang, Wenjie Wan, Yuxiang Zhuang, Zhong Sun, Ju |
| contents | For nonlinear inverse problems that are prevalent in imaging science, symmetries in the forward model are common. When data-driven deep learning approaches are used to solve such problems, these intrinsic symmetries can cause substantial learning difficulties. In this paper, we explain how such difficulties arise and, more importantly, how to overcome them by preprocessing the training set before any learning, i.e., symmetry breaking. We take far-field phase retrieval (FFPR), which is central to many areas of scientific imaging, as an example and show that symmetric breaking can substantially improve data-driven learning. We also formulate the mathematical principle of symmetry breaking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15448 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What is Wrong with End-to-End Learning for Phase Retrieval? Zhang, Wenjie Wan, Yuxiang Zhuang, Zhong Sun, Ju Signal Processing Machine Learning For nonlinear inverse problems that are prevalent in imaging science, symmetries in the forward model are common. When data-driven deep learning approaches are used to solve such problems, these intrinsic symmetries can cause substantial learning difficulties. In this paper, we explain how such difficulties arise and, more importantly, how to overcome them by preprocessing the training set before any learning, i.e., symmetry breaking. We take far-field phase retrieval (FFPR), which is central to many areas of scientific imaging, as an example and show that symmetric breaking can substantially improve data-driven learning. We also formulate the mathematical principle of symmetry breaking. |
| title | What is Wrong with End-to-End Learning for Phase Retrieval? |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2403.15448 |