Regularization-based Pruning
Regularization-Based Pruning is a model compression technique within neural network pruning that belongs to the broader category of regularization methods, distinguished by its incorporation of structured or sparsity-inducing penalties during training to progressively steer model weights toward zero, thereby enabling the systematic removal of redundant or less significant parameters.
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3.3K
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207
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83
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2017–2026
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Fast ConvNets Using Group-Wise Brain Damage
Vadim Lebedev, Victor Lempitsky · 2016 · 404 citations
Channel Pruning via Automatic Structure Search
Mingbao Lin, Rongrong Ji, Yuxin Zhang et al. · 2020 · 244 citations · Full text
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Technical University of Denmark
Kongens Lyngby, Denmark
4 h-index
15 publications
384 citations
Northeastern University
Boston, United States
4 h-index
11 publications
329 citations
Xiamen University
Xiamen, China
4 h-index
11 publications
1.5K citations
Duke University
Durham, United States
3 h-index
4 publications
233 citations

Peking University
Beijing, China
3 h-index
5 publications
396 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
4 publications
151 citations

University of Alberta
Edmonton, Canada
3 h-index
5 publications
184 citations
Institute of Computing Technology
Beijing, China
2 h-index
2 publications
97 citations
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Repository · 14 publications · 451 citations
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5 publications · 433 citations
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3 publications · 104 citations
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2 publications · 108 citations
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TPAMI · 2 publications · 342 citations
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