A Nonlinear Model Compression Scheme Based on Variational Autoencoder for Microwave Data Inversion

We present an inversion algorithm with a deep-learning-based model compression scheme. Models are described with latent parameters of a trained variational autoencoder (VAE) neural network. Given observed data, latent parameters are inverted by minimizing the data misfit cost function using the Gaus...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on antennas and propagation Vol. 70; no. 11; pp. 11059 - 11069
Main Authors:	Guo, Rui, Lin, Zhichao, Li, Maokun, Yang, Fan, Xu, Shenheng, Abubakar, Aria
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Artificial neural networks Computational modeling Cost function Data models Decoding Deep learning Inverse problems inverse scattering inversion algorithm Machine learning Mathematical models model compression Neural networks Newton methods Parameters Permittivity Training variational autoencoder
ISSN:	0018-926X, 1558-2221
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!