Autoencoder and its various variants

被引：183

作者：

Zhang, Sufang ^{[1
]}

Zhai, Junhai ^{[2
]}

Chen, Junfen ^{[2
]}

He, Qiang ^{[3
]}

机构：

[1] China Meteorol Adm, Hebei Branch China Meteorol Adm Training Ctr, Baoding 071000, Peoples R China

[2] Hebei Univ, Coll Math & Informat Sci, Key Lab Machine Learning & Computat Intelligence, Baoding 071002, Peoples R China

[3] Beijing Univ Civil Engn & Architecture, Sch Sci, Beijing 100044, Peoples R China

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2018年

基金：

中国国家自然科学基金;

关键词：

autoencoder; decoder; deep learning; feature learning; generative model;

D O I：

10.1109/SMC.2018.00080

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The concept of autoencoder was originally proposed by LeCun in 1987, early works on autoencoder were used for dimensionality reduction or feature learning. Recently, with the popularity of deep learning research, autoencoder has been brought to the forefront of generative modeling. Many variants of autoencoder have been proposed by different researchers and have been successfully applied in many fields, such as computer vision, speech recognition and natural language processing. In this paper, we present a comprehensive survey on autoencoder and its various variants. Furthermore, we also present the lineage of the surveyed autoencoders. This paper can provide researchers engaged in related works with very valuable help.

引用

页码：415 / 419

页数：5

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