# Compressed Sensing via Measurement-Conditional Generative Models

arXiv: https://arxiv.org/abs/2007.00873 · HTML: https://mlsanity.com/paper/2007.00873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Solving Inverse Problems with Conditional-GAN Prior via Fast Network-Projected Gradient Descent](https://mlsanity.com/paper/2109.01105/) (2021)

## Abstract (arXiv)

A pre-trained generator has been frequently adopted in compressed sensing (CS) due to its ability to effectively estimate signals with the prior of NNs. In order to further refine the NN-based prior, we propose a framework that allows the generator to utilize additional information from a given measurement for prior learning, thereby yielding more accurate prediction for signals. As our framework has a simple form, it is easily applied to existing CS methods using pre-trained generators. We demonstrate through extensive experiments that our framework exhibits uniformly superior performances by large margin and can reduce the reconstruction error up to an order of magnitude for some applications. We also explain the experimental success in theory by showing that our framework can slightly relax the stringent signal presence condition, which is required to guarantee the success of signal recovery.