Improvement of accuracy of well-known convoluational neural networks by efficient hybrid strategy

Ren You Yan, Che Lun Hung, Chun Yuan Lin, Hsiao Hsi Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Convolutional neural networks have existed for many years, but recently they have been developed to a greater depth and width than ever before with the increase in the computing power of graphics processing units. Convolutional neural networks are widely used in a variety of artificial intelligence applications, including in manufacturing, agriculture, and medicine. The use of artificial intelligence in various industrial fields is expected to increase. However, improvements in network training efficiency have not resulted in a reciprocal improvement in computational power for identification applications. This paper proposes several types of neural networks that are based on well-known networks such as AlexNet, GoogleNet, and ResNet, whose characteristics have been captured and implemented in lower layer neural networks. From the experimental results, using these hybrid neural networks can bring improved accuracy, with well optimized computational time costs compared to networks that require a large amount of computation.

Original languageEnglish
Title of host publicationProceedings - 2018 15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages350-355
Number of pages6
ISBN (Electronic)9781538685341
DOIs
StatePublished - 2 Jul 2018
Event15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018 - Yichang, China
Duration: 16 Oct 201818 Oct 2018

Publication series

NameProceedings - 2018 15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018

Conference

Conference15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018
Country/TerritoryChina
CityYichang
Period16/10/1818/10/18

Keywords

  • Artificial Intelligence
  • Convolutional Neural Network
  • Deep learning
  • Image Classification

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