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Channel pruning is a popular technique for compressing convolutional neural networks (CNNs), where various pruning criteria have been proposed to remove the redundant filters.
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang · 2016
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Pruning convolutional neural networks for resource efficient inference
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Sebastian Lutz, Konstantinos Amplianitis, and Aljosa Smolic · 2018
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Filter pruning via geometric median for deep convolutional neural networks acceleration
Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Importance estimation for neural network pruning
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Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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Geometric median in nearly linear time
Michael B Cohen, Yin Tat Lee, Gary Miller, Jakub Pachocki, and Aaron Sidford · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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An entropy-based pruning method for cnn compression
Jian-Hao Luo and Jianxin Wu · 2017
Cited alongside, same era.
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz · 2019
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Global sparse momentum sgd for pruning very deep neural networks
Xiaohan Ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, Ji Liu, et al · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Importance estimation for neural network pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz · 2019
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Cop: Customized deep model compression via regularized correlation-based filter-level pruning
Wenxiao Wang, Cong Fu, Jishun Guo, Deng Cai, and Xiaofei He · 2019
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Luck matters: Understanding training dynamics of deep relu networks
Yuandong Tian, Tina Jiang, Qucheng Gong, and Ari Morcos · 2019
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Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Dianet: Dense-and-implicit attention network
Zhongzhan Huang, Senwei Liang, Mingfu Liang, and Haizhao Yang · 2019
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Srm: A style-based recalibration module for convolutional neural networks
HyunJae Lee, Hyo-Eun Kim, and Hyeonseob Nam · 2019
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Instance enhancement batch normalization: an adaptive regulator of batch noise
Senwei Liang, Zhongzhan Huang, Mingfu Liang, and Haizhao Yang · 2019
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Spatial group-wise enhance: Improving semantic feature learning in convolutional networks
Xiang Li, Xiaolin Hu, and Jian Yang · 2019
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Cap: Context-aware pruning for semantic segmentation
Wei He, Meiqing Wu, Mingfu Liang, and Siew-Kei Lam · 2020
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Comparing fine-tuning and rewinding in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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Zechun Liu, Xiangyu Zhang, Zhiqiang Shen, Zhe Li, Yichen Wei, Kwang-Ting Cheng, and Jian Sun · 2020
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Group sparsity: The hinge between filter pruning and decomposition for network compression
Yawei Li, Shuhang Gu, Christoph Mayer, Luc Van Gool, and Radu Timofte · 2020
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Learning filter pruning criteria for deep convolutional neural networks acceleration
Yang He, Yuhang Ding, Ping Liu, Linchao Zhu, Hanwang Zhang, and Yi Yang · 2020
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Rethinking class-discrimination based cnn channel pruning
Yuchen Liu, David Wentzlaff, and SY Kung · 2020
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Eagleeye: Fast sub-net evaluation for efficient neural network pruning
Bailin Li, Bowen Wu, Jiang Su, Guangrun Wang, and Liang Lin · 2020
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Towards efficient model compression via learned global ranking
Ting-Wu Chin, Ruizhou Ding, Cha Zhang, and Diana Marculescu · 2020
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