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Squeeze-and-Excitation Networks
Hu, Jie1,2,3; Shen, Li7; Albanie, Samuel7; Sun, Gang3,6; Wu, Enhua1,2,4,5
Corresponding AuthorShen, Li(
AbstractThe central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We show that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. We further demonstrate that SE blocks bring significant improvements in performance for existing state-of-the-art CNNs at slight additional computational cost. Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251 percent, surpassing the winning entry of 2016 by a relative improvement of similar to 25 percent. Models and code are available at
KeywordSqueeze-and-excitation image representations attention convolutional neural networks
Indexed BySCI
Funding ProjectNSFC[61632003] ; NSFC[61620106003] ; NSFC[61672502] ; NSFC[61571439] ; National Key R&D Program of China[2017YFB1002701] ; Macao FDCT Grant[068/2015/A2] ; EPSRC AIMS CDT[EP/L015897/1]
Funding OrganizationNSFC ; National Key R&D Program of China ; Macao FDCT Grant ; EPSRC AIMS CDT
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000545415400015
Citation statistics
Cited Times:4643[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorShen, Li
Affiliation1.Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Momenta, Dongsheng Plaza A,8 Zhongguancun East Rd, Beijing 100083, Peoples R China
4.Univ Macau, Fac Sci & Technol, Taipa, Macao, Peoples R China
5.Univ Macau, AI Ctr, Taipa, Macao, Peoples R China
6.Chinese Acad Sci, Inst Automat, LIAMA NLPR, Beijing 100190, Peoples R China
7.Univ Oxford, Visual Geometry Grp, Oxford OX1 2JD, England
Recommended Citation
GB/T 7714
Hu, Jie,Shen, Li,Albanie, Samuel,et al. Squeeze-and-Excitation Networks[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2020,42(8):2011-2023.
APA Hu, Jie,Shen, Li,Albanie, Samuel,Sun, Gang,&Wu, Enhua.(2020).Squeeze-and-Excitation Networks.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,42(8),2011-2023.
MLA Hu, Jie,et al."Squeeze-and-Excitation Networks".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 42.8(2020):2011-2023.
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