End-to-End Learning for Phase Retrieval [conference paper]

Conference

ICML workshop on ML Interpretability for Scientific Discovery - July 17, 2020

Authors

Raunak Manekar (Ph.D. student), Kshitij Tayal (Ph.D. student), Vipin Kumar (professor), Ju Sun (assistant professor)

Abstract

We consider the end-to-end deep learning approach for phase retrieval, a central problem in scientific imaging. We highlight a fundamental difficulty for learning that previous work has neglected, likely due to the biased datasets they use for training and evaluation. We propose a simple yet different formulation for PR that seems to overcome the difficulty and return consistently better qualitative results.

Link to full paper

End-to-End Learning for Phase Retrieval

Keywords

machine learning, phase retrieval

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