Compressed Sensing of Generative Sparse-latent (GSL) Signals
Machine Learning
2023-10-24 v1 Artificial Intelligence
Abstract
We consider reconstruction of an ambient signal in a compressed sensing (CS) setup where the ambient signal has a neural network based generative model. The generative model has a sparse-latent input and we refer to the generated ambient signal as generative sparse-latent signal (GSL). The proposed sparsity inducing reconstruction algorithm is inherently non-convex, and we show that a gradient based search provides a good reconstruction performance. We evaluate our proposed algorithm using simulated data.
Keywords
Cite
@article{arxiv.2310.15119,
title = {Compressed Sensing of Generative Sparse-latent (GSL) Signals},
author = {Antoine Honoré and Anubhab Ghosh and Saikat Chatterjee},
journal= {arXiv preprint arXiv:2310.15119},
year = {2023}
}
Comments
Accepted at 31st European Signal Processing Conference, EUSIPCO 2023