Random convolutional neural network based on distributed computing with decentralized architecture

Yige Xu, Huijuan Lu*, Minchao Ye, Ke Yan, Zhigang Gao, Qun Jin

*この研究の対応する著者

研究成果

抄録

In recent years, deep learning has made great progress in image classification and detection. Popular deep learning algorithms rely on deep networks and multiple rounds of back-propagations. In this paper, we propose two approaches to accelerate deep networks. One is expanding the width of every layer. We reference to the Extreme Learning Machine, setting big number of convolution kernels to extract features in parallel. It can obtain multiscale features and improve network efficiency. The other is freezing part of layers. It can reduce back-propagations and speed up the training procedure. From the above, it is a random convolution architecture that network is proposed for image classification. In our architecture, every combination of random convolutions extracts distinct features. Apparently, we need a lot of experiments to choose the best combination. However, centralized computing may limit the number of combinations. Therefore, a decentralized architecture is used to enable the use of multiple combinations.

本文言語English
ホスト出版物のタイトルHuman Centered Computing - 5th International Conference, HCC 2019, Revised Selected Papers
編集者Danijela Miloševic, Yong Tang, Qiaohong Zu
出版社Springer
ページ504-510
ページ数7
ISBN(印刷版)9783030374280
DOI
出版ステータスPublished - 2019
イベント5th International Conference on Human Centered Computing, HCC 2019 - Čačak, Serbia
継続期間: 2019 8 52019 8 7

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11956 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference5th International Conference on Human Centered Computing, HCC 2019
国/地域Serbia
CityČačak
Period19/8/519/8/7

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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