Search Results for author:
Found 4 papers, 2 papers with code
Misspecified Phase Retrieval with Generative Priors
In this paper, we study phase retrieval under model misspecification and generative priors.
Non-Iterative Recovery from Nonlinear Observations using Generative Models
In this paper, we aim to estimate the direction of an underlying signal from its nonlinear observations following the semi-parametric single index model (SIM).
Generative Principal Component Analysis
We perform experiments on various image datasets for spiked matrix and phase retrieval models, and illustrate performance gains of our method to the classic power method and the truncated power method devised for sparse principal component analysis.
Rethinking Medical Image Reconstruction via Shape Prior, Going Deeper and Faster: Deep Joint Indirect Registration and Reconstruction
We also introduce registration blocks based deep nets to predict the registration parameters and warp transformation accurately and efficiently.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
