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dc.contributor.author
Li, Yawei
dc.contributor.author
Gu, Shuhang
dc.contributor.author
Mayer, Christoph
dc.contributor.author
Van Gool, Luc
dc.contributor.author
Timofte, Radu
dc.date.accessioned
2020-12-18T10:10:48Z
dc.date.available
2020-12-14T21:09:13Z
dc.date.available
2020-12-18T10:10:48Z
dc.date.issued
2020
dc.identifier.isbn
978-1-7281-7168-5
en_US
dc.identifier.isbn
978-1-7281-7169-2
en_US
dc.identifier.other
10.1109/CVPR42600.2020.00804
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/456269
dc.description.abstract
In this paper, we analyze two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense. By simply changing the way the sparsity regularization is enforced, filter pruning and low-rank decomposition can be derived accordingly. This provides another flexible choice for network compression because the techniques complement each other. For example, in popular network architectures with shortcut connections (e.g. ResNet), filter pruning cannot deal with the last convolutional layer in a ResBlock while the low-rank decomposition methods can. In addition, we propose to compress the whole network jointly instead of in a layer-wise manner. Our approach proves its potential as it compares favorably to the state-of-the-art on several benchmarks. Code is available at https://github.com/ofsoundof/group_sparsity.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.title
Group sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
en_US
dc.type
Conference Paper
dc.date.published
2020-08-05
ethz.book.title
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
en_US
ethz.pages.start
8015
en_US
ethz.pages.end
8024
en_US
ethz.event
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) (virtual)
en_US
ethz.event.location
Seattle, WA, USA
en_US
ethz.event.date
June 13-19, 2020
en_US
ethz.notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
en_US
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03514 - Van Gool, Luc / Van Gool, Luc
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03514 - Van Gool, Luc / Van Gool, Luc
en_US
ethz.date.deposited
2020-12-14T21:09:41Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2020-12-18T10:10:57Z
ethz.rosetta.lastUpdated
2021-02-15T22:42:18Z
ethz.rosetta.versionExported
true
ethz.COinS
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