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

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題Proceedings - 2018 15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面350-355
頁數6
ISBN(電子)9781538685341
DOIs
出版狀態Published - 5 2月 2019
事件15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018 - Yichang, China
持續時間: 16 10月 201818 10月 2018

出版系列

名字Proceedings - 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
國家/地區China
城市Yichang
期間16/10/1818/10/18

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