The Difference Learning of Hidden Layer between Autoencoder and Variational Autoencoder

被引：0

作者：

Xu, Qingyang ^{[1
]}

Wu, Zhe ^{[1
]}

Yang, Yiqin ^{[1
]}

Zhang, Li ^{[1
]}

机构：

[1] Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China

来源：

2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年

基金：

英国工程与自然科学研究理事会;

关键词：

Autoencoder; Variational autoencoder; Hidden layer learning; Probabilistic model; MNIST;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Autoencoder is an excellent unsupervised learning algorithm. However, it can not generate kinds of sample data in the decoding process. Variational autoencoder is a typical generative adversarial net which can generate various data to augment the sample data. In this paper, we want to do some research about the information learning in hidden layer. In the simulation, we compare the hidden layer learning of hidden layer in conventional autoencoder and variational autoencoder.

引用

页码：4801 / 4804

页数：4

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Doersch, 2016, ARXIV160605908, DOI DOI 10.3389/FPHYS.2016.00108

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COMMUNICATIONS OF THE ACM, 2020, 63 (11) :139-144

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