What is Wrong with End-to-End Learning for Phase Retrieval?

arXiv:2403.15448v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cs.lg data data-driven deep learning eess.sp imaging intrinsic paper retrieval science solve type