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
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Ye, Jianbo , Lu, Xin , Lin, Zhe and Wang, James Z
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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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...
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Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
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TN_cdi_proquest_journals_2071308422
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2071308422
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2331-8422