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Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2071308422

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

About this item

Full title

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2018-02

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

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Scope and Contents

Contents

Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions in resource-limited scenarios. A widely-used practice in relevant work assumes that a smaller-norm parameter or feature plays a less informative role at the inference time. In this paper, we propose a chann...

Alternative Titles

Full title

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

Authors, Artists and Contributors

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Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2071308422

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2071308422

Other Identifiers

E-ISSN

2331-8422

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