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Group Sparsity: A Unified Framework for Network Pruning and Neural Architecture Search

  • We demonstrate how to exploit group sparsity in order to bridge the areas of network pruning and neural architecture search (NAS). This results in a new one-shot NAS optimizer that casts the problem as a single-level optimization problem and does not suffer any performance degradation from discretizating the architecture.

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/5285
Bibliografische Angaben
Title (English):Group Sparsity: A Unified Framework for Network Pruning and Neural Architecture Search
Conference:Workshop on Neural Architecture Search: IEEE/CVF Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 20-25 June 2021
Author:Avraam Chatzimichailidis, Arber Zela, Shalini Shalini, Peter Labus, Janis KeuperStaff MemberORCiDGND, Frank Hutter, Yang Yang
Year of Publication:2021
Contributing Corporation:IEEE, Computer Vision Foundation
Page Number:4
First Page:1
Last Page:4
Parent Title (English):CVPR2021-NAS: Computer Society Conference on Computer Vision and Pattern Recognition : Workshop on Neural Architecture Search
URL:https://ml.informatik.uni-freiburg.de/wp-content/uploads/2022/03/Group_Sparsity.pdf
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Institutes:Bibliografie
Formale Angaben
Open Access: Open Access 
Licence (German):License LogoUrheberrechtlich geschützt