title: Compressed Sensing with Deep Image Prior and Learned Regularization
abstract: We propose a novel method for compressed sensing recovery using
  untrained deep generative models. Our method is based on the recently
  proposed Deep Image Prior (DIP), wherein the convolutional weights of
  the network are optimized to match the observed measurements. We show
  that this approach can be applied to solve any differentiable linear inverse
  problem, outperforming previous unlearned methods. Unlike various learned approaches based on generative models, our method does not require pre-training over large datasets. We further introduce a novel learned regularization technique, which incorporates prior information on the network weights. This reduces reconstruction error, especially for noisy measurements. Finally we prove that, using the DIP optimization approach, moderately overparameterized single-layer networks trained can perfectly fit any signal despite the nonconvex nature of the fitting problem. This theoretical result provides justification for early stopping.
authors: Dave Van Veen Ajil Jalal Mahdi Soltanolkotabi Eric Price Sriram Vishwanath Alexandros G. Dimakis
hIndex: Alexandros G. Dimakis 55