Autoencoders
Machine Learning
2021-04-06 v2 Computer Vision and Pattern Recognition
Machine Learning
Abstract
An autoencoder is a specific type of a neural network, which is mainly designed to encode the input into a compressed and meaningful representation, and then decode it back such that the reconstructed input is similar as possible to the original one. This chapter surveys the different types of autoencoders that are mainly used today. It also describes various applications and use-cases of autoencoders.
Cite
@article{arxiv.2003.05991,
title = {Autoencoders},
author = {Dor Bank and Noam Koenigstein and Raja Giryes},
journal= {arXiv preprint arXiv:2003.05991},
year = {2021}
}
Comments
Book chapter