中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 中国科学院分子影像重点实验室
PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural Networks

文献类型：期刊论文

作者Xiao, Anqi1,2; Shen, Biluo1,2; Tian, Jie1,2,3,4; Hu, Zhenhua1,2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2023-04-26
页码13
ISSN号2162-237X
关键词Convolution Search problems Optimization Computer architecture Object detection Molecular imaging Visualization Multiscale neural architecture search (NAS) plug-and-play representation learning
DOI10.1109/TNNLS.2023.3264551
通讯作者Tian, Jie(tian@ieee.org) ; Hu, Zhenhua(zhenhua.hu@ia.ac.cn)
英文摘要Multiscale features are of great importance in modern convolutional neural networks, showing consistent performance gains on numerous vision tasks. Therefore, many plug-and-play blocks are introduced to upgrade existing convolutional neural networks for stronger multiscale representation ability. However, the design of plug-and-play blocks is getting more and more complex, and these manually designed blocks are not optimal. In this work, we propose PP-NAS to develop plug-and-play blocks based on neural architecture search (NAS). Specifically, we design a new search space PPConv and develop a search algorithm consisting of one-level optimization, zero-one loss, and connection existence loss. PP-NAS minimizes the optimization gap between super-net and subarchitectures and can achieve good performance even without retraining. Extensive experiments on image classification, object detection, and semantic segmentation verify the superiority of PP-NAS over state-of-the-art CNNs (e.g., ResNet, ResNeXt, and Res2Net). Our code is available at https://github.com/ainieli/PP-NAS.
资助项目National Natural Science Foundation of China (NSFC)[92059207] ; National Natural Science Foundation of China (NSFC)[62027901] ; National Natural Science Foundation of China (NSFC)[81930053] ; National Natural Science Foundation of China (NSFC)[81227901] ; Chinese Academy of Sciences (CAS) Youth Interdisciplinary Team[JCTD-2021-08] ; Zhuhai High-Level Health Personnel Team Project[HLHPTP201703] ; Google's TPU Research Cloud (TRC)
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000980562400001
资助机构National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) Youth Interdisciplinary Team ; Zhuhai High-Level Health Personnel Team Project ; Google's TPU Research Cloud (TRC)
源URL[http://ir.ia.ac.cn/handle/173211/53236]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie; Hu, Zhenhua
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing Key Lab Mol Imaging, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian 710071, Peoples R China
4.Beihang Univ, Sch Engn Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
推荐引用方式
GB/T 7714		 Xiao, Anqi,Shen, Biluo,Tian, Jie,et al. PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural Networks[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:13.
APA Xiao, Anqi,Shen, Biluo,Tian, Jie,&Hu, Zhenhua.(2023).PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural Networks.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,13.
MLA Xiao, Anqi,et al."PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural Networks".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):13.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。