Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017)

Relationship between Complexity and Precision of Convolutional Neural Networks

Authors
Xiaolong Ke, Wenming Cao, Fangfang Lv
Corresponding Author
Xiaolong Ke
Available Online March 2017.
DOI
10.2991/isaeece-17.2017.62How to use a DOI?
Keywords
Convolutional neural networks, image classification, complexity control, complexity-precision modeling
Abstract

Convolutional neural networks (CNNs) have been successfully applied to the computer vision areas in recent years. However, these high performing CNNs generally involve intensive computation, which is unaffordable for many real-time applications. In this paper, we study the impact of four important network parameters . Then we develop mathematical models to characterize the relationship and tradeoff between the complexity C and precision P of CNNs. Once the models C( ) and P( ) are obtained, we are able to perform complexity-precision optimization to minimize the CNN complexity while achieving the target precision level by selecting the optimal configuration of four network parameters .

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017)
Series
Advances in Engineering Research
Publication Date
March 2017
ISBN
978-94-6252-334-0
ISSN
2352-5401
DOI
10.2991/isaeece-17.2017.62How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xiaolong Ke
AU  - Wenming Cao
AU  - Fangfang Lv
PY  - 2017/03
DA  - 2017/03
TI  - Relationship between Complexity and Precision of Convolutional Neural Networks
BT  - Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017)
PB  - Atlantis Press
SP  - 325
EP  - 329
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-17.2017.62
DO  - 10.2991/isaeece-17.2017.62
ID  - Ke2017/03
ER  -