Manipulating identical filter redundancy for efficient pruning on deep and complicated CNN

Hao, T., Ding, X., Han, J. et al. (2 more authors) (2024) Manipulating identical filter redundancy for efficient pruning on deep and complicated CNN. IEEE Transactions on Neural Networks and Learning Systems, 35 (11). pp. 16831-16844. ISSN 2162-237X

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Hao, T.
  • Ding, X.
  • Han, J.
  • Guo, Y.
  • Ding, G.
Copyright, Publisher and Additional Information:

© 2023 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Neural Networks and Learning Systems is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: Deep Learning; Convolutional Neural Network; Model Compression; Filter Pruning; Channel Pruning
Dates:
  • Published: November 2024
  • Published (online): 12 October 2023
  • Accepted: 11 July 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 20 Jul 2023 11:29
Last Modified: 01 Nov 2024 10:48
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: 10.1109/TNNLS.2023.3298263
Related URLs:
Open Archives Initiative ID (OAI ID):

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