版权说明 操作指南
首页 > 成果 > 详情

A Width-Growth Model with Subnetwork Nodes and Refinement Structure for Representation Learning and Image Classification

认领
导出
Link by 万方学术期刊
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Zhang, Wandong;Wu, Q. M. Jonathan*;Yang, Yimin;Akilan, Thangarajah;Zhang, Hui
通讯作者:
Wu, Q. M. Jonathan
作者机构:
[Zhang, Wandong; Wu, Q. M. Jonathan] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada.
[Yang, Yimin; Akilan, Thangarajah] Lakehead Univ, Dept Comp Sci, Thunder Bay, ON P7B 5E1, Canada.
[Zhang, Hui] Hunan Univ, Sch Robot, Changsha 410082, Peoples R China.
[Zhang, Hui] Changsha Univ Sci & Technol, Coll Elect & Informat Engn, Changsha 410012, Peoples R China.
通讯机构:
[Wu, Q. M. Jonathan] U
Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada.
语种:
英文
关键词:
Encoding;Feature extraction;Informatics;Neural networks;Learning systems;Nonhomogeneous media;Transforms;Feature refinement;image classification;multimodal fusion;representation learning;subnetwork neural network
期刊:
IEEE Transactions on Industrial Informatics
ISSN:
1551-3203
年:
2021
卷:
17
期:
3
页码:
1562-1572
基金类别:
Manuscript received July 30, 2019; revised December 4, 2019 and January 29, 2020; accepted March 22, 2020. Date of publication March 30, 2020; date of current version November 20, 2020. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada, in part by the National Natural Science Foundation of China under Grant 61971071, in part by the National Key R&D Program of China under Grant 2018YFB1308200, in part by the Hunan Key Project of Research and Development Plan under Grant 2018GK2022, and in part by the Changsha Science and Technology Project under Grant kq190708. Paper no. TII-19-3325. (Corresponding author: Q. M. Jonathan Wu.) Wandong Zhang and Q. M. Jonathan Wu are with the Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada (e-mail: zhang1lq@uwindsor.ca; jwu@ uwindsor.ca).
机构署名:
本校为其他机构
院系归属:
电气与信息工程学院
摘要:
This article presents a new supervised multilayer subnetwork-based feature refinement and classification model for representation learning. The novelties of this algorithm are as follows: 1) different from most multilayer networks that go deeper with increased number of network layers, this work architects a model with wider subnetwork nodes; 2) the conventional classification methods adopt a separate search mechanism to derive a generalized feature space and to get the final cognition, but this work proposes a one-shot process to find the meaningful latent space and recognize the objects; and...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com