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Pruning is a promising approach to compress complex deep learning models in order to deploy them on resource-constrained edge devices.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Song Han, Huizi Mao, and William J Dally · 2015
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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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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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N2n learning: Network to network compression via policy gradient reinforcement learning
Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, and Kris M Kitani · 2017
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Reinforcement learning for architecture search by network transformation. corr abs/1707.04873 (2017)
H Cai, T Chen, W Zhang, Y Yu, and J Wang · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Deftnn: Addressing bottlenecks for dnn execution on gpus via synapse vector elimination and near-compute data fission
P. Hill, A. Jain, M. Hill, B. Zamirai, C. Hsu, M. A. Laurenzano, S. Mahlke, L. Tang, and J. Mars · 2017
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Data-driven sparse structure selection for deep neural networks
Zehao Huang and Naiyan Wang · 2018
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Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Faster gaze prediction with dense networks and fisher pruning
Lucas Theis, Iryna Korshunova, Alykhan Tejani, and Ferenc Huszár · 2018
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Discrimination-aware channel pruning for deep neural networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, and Jinhui Zhu · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Variational convolutional neural network pruning
Chenglong Zhao, Bingbing Ni, Jian Zhang, Qiwei Zhao, Wenjun Zhang, and Qi Tian · 2019
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Discrete model compression with resource constraint for deep neural networks
Shangqian Gao, Feihu Huang, Jian Pei, and Heng Huang · 2020
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Multi-dimensional pruning: A unified framework for model compression
Jinyang Guo, Wanli Ouyang, and Dong Xu · 2020
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Operation-aware soft channel pruning using differentiable masks
Minsoo Kang and Bohyung Han · 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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Hrank: Filter pruning using high-rank feature map
Mingbao Lin, Rongrong Ji, Yan Wang, Yichen Zhang, Baochang Zhang, Yonghong Tian, and Ling Shao · 2020
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Stabilizing the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin · 2019
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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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Provable filter pruning for efficient neural networks
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, and Daniela Rus · 2019
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Towards optimal structured cnn pruning via generative adversarial learning
Shaohui Lin, Rongrong Ji, Chenqian Yan, Baochang Zhang, Liujuan Cao, Qixiang Ye, Feiyue Huang, and David Doermann · 2019
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Eigendamage: Structured pruning in the kronecker-factored eigenbasis
Chaoqi Wang, Roger Grosse, Sanja Fidler, and Guodong Zhang · 2019
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Learning sparser neural network with differentiable scale-invariant sparsity measures
H Yang, W Wen, and H Deephoyer Li · 2019
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Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks
Zhonghui You, Kun Yan, Jinmian Ye, Meng Ma, and Ping Wang · 2019
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Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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Scop: Scientific control for reliable neural network pruning
Yehui Tang, Yunhe Wang, Yixing Xu, Dacheng Tao, Chunjing Xu, Chao Xu, and Chang Xu · 2020
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Pruning from scratch
Yulong Wang, Xiaolu Zhang, Lingxi Xie, Jun Zhou, Hang Su, Bo Zhang, and Xiaolin Hu · 2020
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Neuron-level structured pruning using polarization regularizer
Tao Zhuang, Zhixuan Zhang, Yuheng Huang, Xiaoyi Zeng, Kai Shuang, and Xiang Li · 2020
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Gdp: Stabilized neural network pruning via gates with differentiable polarization
Yi Guo, Huan Yuan, Jianchao Tan, Zhangyang Wang, Sen Yang, and Ji Liu · 2021
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