Binary Volumetric Convolutional Neural Networks for 3-D Object Recognition

doi:10.1109/TIM.2018.2840598

	Binary Volumetric Convolutional Neural Networks for 3-D Object Recognition
	Ma, Chao 1; Guo, Yulan 1,2; Lei, Yinjie 3; An, Wei 1
	2019
发表期刊	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN	0018-9456
卷号	68 期号:1 页码:38-48
摘要	To address the high computational and memory cost in 3-D volumetric convolutional neural networks (CNNs), we propose an approach to train binary volumetric CNNs for 3-D object recognition. Our method is specifically designed for 3-D data, in which it transforms the inputs and weights in convolutional/fully connected layers to binary values, which can potentially accelerate the networks by efficient bitwise operations. Two loss calculation methods are designed to solve the accuracy decrease problem when the weights in the last layer are binarized. Four binary volumetric CNNs are obtained from their corresponding floating-point networks using our approach. Evaluations on three public datasets from different domains (Computer Aided Design (CAD), light detection and ranging (LiDAR), and RGB-D) show that our binary volumetric CNNs can achieve a comparable recognition performance as their floating-point counterparts but consume less computational and memory resources.
关键词	3-D object recognition convolutional neural network (CNN) deep learning (DL) network binarization volumetric representation
DOI	10.1109/TIM.2018.2840598
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61403265] ; National Natural Science Foundation of China[61602499] ; National Natural Science Foundation of China[61471371] ; Science and Technology Plan of Sichuan Province[2015SZ0226] ; National Postdoctoral Program for Innovative Talents[BX201600172] ; China Postdoctoral Science Foundation
WOS研究方向	Engineering ; Instruments & Instrumentation
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000452611600003
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：44[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/3515
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Guo, Yulan
作者单位	1.Natl Univ Def Technol, Coll Elect Sci, Changsha 410073, Hunan, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610065, Sichuan, Peoples R China
推荐引用方式 GB/T 7714	Ma, Chao,Guo, Yulan,Lei, Yinjie,et al. Binary Volumetric Convolutional Neural Networks for 3-D Object Recognition[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2019,68(1):38-48.
APA	Ma, Chao,Guo, Yulan,Lei, Yinjie,&An, Wei.(2019).Binary Volumetric Convolutional Neural Networks for 3-D Object Recognition.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,68(1),38-48.
MLA	Ma, Chao,et al."Binary Volumetric Convolutional Neural Networks for 3-D Object Recognition".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 68.1(2019):38-48.