Pruning convolution neural networks using filter clustering based on normalized cross-correlation si...
Pruning convolution neural networks using filter clustering based on normalized cross-correlation similarity
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Publisher
Taylor & Francis Group
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Language
English
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Taylor & Francis Group
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Contents
Despite all the recent development and success of deep neural networks, deployment of a deep model onto the resource-constrained devices still remains challenging. However, model pruning can resolve this issue for Convolutional Neural Networks (CNNs), since it is one of the most popular approaches to reducing computational complexities. Therefore,...
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Full title
Pruning convolution neural networks using filter clustering based on normalized cross-correlation similarity
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TN_cdi_doaj_primary_oai_doaj_org_article_75d70d0a7d4e4e1cb34ea813312b971b
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_75d70d0a7d4e4e1cb34ea813312b971b
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ISSN
2475-1839
E-ISSN
2475-1847
DOI
10.1080/24751839.2024.2415008
