Random convolutional neural network based on distributed computing with decentralized architecture

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationHuman Centered Computing - 5th International Conference, HCC 2019, Revised Selected Papers
EditorsDanijela Miloševic, Yong Tang, Qiaohong Zu
PublisherSpringer
Pages504-510
Number of pages7
ISBN (Print)9783030374280
DOIs
Publication statusPublished - 2019
Event5th International Conference on Human Centered Computing, HCC 2019 - Čačak, Serbia
Duration: 2019 Aug 52019 Aug 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11956 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

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

Keywords

  • Decentralized architecture
  • Distributed computing
  • Random convolution

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Xu, Y., Lu, H., Ye, M., Yan, K., Gao, Z., & Jin, Q. (2019). Random convolutional neural network based on distributed computing with decentralized architecture. In D. Miloševic, Y. Tang, & Q. Zu (Eds.), Human Centered Computing - 5th International Conference, HCC 2019, Revised Selected Papers (pp. 504-510). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11956 LNCS). Springer. https://doi.org/10.1007/978-3-030-37429-7_50