Unlocking Inverse Problems Using Deep Learning: Breaking Symmetries in Phase Retrieval
Abstract
In many physical systems, inputs related by intrinsic system symmetries generate the same output. So when inverting such systems, an input is mapped to multiple symmetry-related outputs. This causes fundamental difficulty in tackling these inverse problems by the emerging end-to-end deep learning approach. Taking phase retrieval as an illustrative example, we show that careful symmetry breaking on the training data can help get rid of the difficulty and significantly improve learning performance on real data.
Cite
Text
Tayal et al. "Unlocking Inverse Problems Using Deep Learning: Breaking Symmetries in Phase Retrieval." NeurIPS 2020 Workshops: Deep_Inverse, 2020.Markdown
[Tayal et al. "Unlocking Inverse Problems Using Deep Learning: Breaking Symmetries in Phase Retrieval." NeurIPS 2020 Workshops: Deep_Inverse, 2020.](https://mlanthology.org/neuripsw/2020/tayal2020neuripsw-unlocking/)BibTeX
@inproceedings{tayal2020neuripsw-unlocking,
title = {{Unlocking Inverse Problems Using Deep Learning: Breaking Symmetries in Phase Retrieval}},
author = {Tayal, Kshitij and Lai, Chieh-Hsin and Manekar, Raunak and Zhuang, Zhong and Kumar, Vipin and Sun, Ju},
booktitle = {NeurIPS 2020 Workshops: Deep_Inverse},
year = {2020},
url = {https://mlanthology.org/neuripsw/2020/tayal2020neuripsw-unlocking/}
}