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Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks.
The transitive reduction of a directed graph
Alfred V. Aho, Michael R Garey, and Jeffrey D. Ullman · 1972
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Torchvision the machine-vision package of torch
Sébastien Marcel and Yann Rodriguez · 2010
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Long short-term memory
Alex Graves · 2012
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 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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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Quantized convolutional neural networks for mobile devices
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng · 2016
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Pan · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 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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Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Auto-balanced filter pruning for efficient convolutional neural networks
Xiaohan Ding, Guiguang Ding, Jungong Han, and Sheng Tang · 2018
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Soft filter pruning for accelerating deep convolutional neural networks
Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang · 2018
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Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
Cited alongside, same era.
Data-driven sparse structure selection for deep neural networks
Zehao Huang and Naiyan Wang · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Netadapt: Platform-aware neural network adaptation for mobile applications
Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, and Hartwig Adam · 2018
Cited alongside, same era.
Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
Jianbo Ye, Xin Lu, Zhe Lin, and James Z Wang · 2018
Cited alongside, same era.
Neural network pruning with residual-connections and limited-data
Jian-Hao Luo and Jianxin Wu · 2020
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Logarithmic pruning is all you need
Laurent Orseau, Marcus Hutter, and Omar Rivasplata · 2020
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Lookahead: a far-sighted alternative of magnitude-based pruning
Sejun Park, Jaeho Lee, Sangwoo Mo, and Jinwoo Shin · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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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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Movement pruning: Adaptive sparsity by fine-tuning
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Nisp: Pruning networks using neuron importance score propagation
Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, and Larry S Davis · 2018
Cited alongside, same era.
Centripetal sgd for pruning very deep convolutional networks with complicated structure
Xiaohan Ding, Guiguang Ding, Yuchen Guo, and Jungong Han · 2019
Cited alongside, same era.
Approximated oracle filter pruning for destructive cnn width optimization
Xiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, and Chenggang Yan · 2019
Cited alongside, same era.
Network pruning via transformable architecture search
Xuanyi Dong and Yi Yang · 2019
Cited alongside, same era.
Filter pruning via geometric median for deep convolutional neural networks acceleration
Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang · 2019
Cited alongside, same era.
A signal propagation perspective for pruning neural networks at initialization
Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, and Philip HS Torr · 2019
Cited alongside, same era.
Metapruning: Meta learning for automatic neural network channel pruning
Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Kwang-Ting Cheng, and Jian Sun · 2019
Cited alongside, same era.
Victor Sanh, Thomas Wolf, and Alexander Rush · 2020
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Parameterized structured pruning for deep neural networks
Günther Schindler, Wolfgang Roth, Franz Pernkopf, and Holger Fröning · 2020
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Neural pruning via growing regularization
Huan Wang, Can Qin, Yulun Zhang, and Yun Fu · 2020
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Distilling knowledge from graph convolutional networks
Yiding Yang, Jiayan Qiu, Mingli Song, Dacheng Tao, and Xinchao Wang · 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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Resrep: Lossless cnn pruning via decoupling remembering and forgetting
Xiaohan Ding, Tianxiang Hao, Jianchao Tan, Ji Liu, Jungong Han, Yuchen Guo, and Guiguang Ding · 2021
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Network pruning via performance maximization
Shangqian Gao, Feihu Huang, Weidong Cai, and Heng Huang · 2021
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Meta-aggregator: Learning to aggregate for 1-bit graph neural networks
Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, and Dacheng Tao · 2021
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Pruning and quantization for deep neural network acceleration: A survey
Tailin Liang, John Glossner, Lei Wang, Shaobo Shi, and Xiaotong Zhang · 2021
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Group fisher pruning for practical network compression
Liyang Liu, Shilong Zhang, Zhanghui Kuang, Aojun Zhou, Jing-Hao Xue, Xinjiang Wang, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang · 2021
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Accelerate cnns from three dimensions: a comprehensive pruning framework
Wenxiao Wang, Minghao Chen, Shuai Zhao, Long Chen, Jinming Hu, Haifeng Liu, Deng Cai, Xiaofei He, and Wei Liu · 2021
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Joint-detnas: upgrade your detector with nas, pruning and dynamic distillation
Lewei Yao, Renjie Pi, Hang Xu, Wei Zhang, Zhenguo Li, and Tong Zhang · 2021
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Aligned structured sparsity learning for efficient image super-resolution
Yulun Zhang, Huan Wang, Can Qin, and Yun Fu · 2021
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Dataset distillation via factorization
Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, and Xinchao Wang · 2022
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Cp-vit: Cascade vision transformer pruning via progressive sparsity prediction
Zhuoran Song, Yihong Xu, Zhezhi He, Li Jiang, Naifeng Jing, and Xiaoyao Liang · 2022
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Factorizing knowledge in neural networks
Xingyi Yang, Jingwen Ye, and Xinchao Wang · 2022
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Deep model reassembly
Xingyi Yang, Daquan Zhou, Songhua Liu, Jingwen Ye, and Xinchao Wang · 2022
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Slimmable dataset condensation
Songhua Liu, Jingwen Ye, Runpeng Yu, and Xinchao Wang · 2023
Closest in time.
Huan Wang, Can Qin, Yue Bai, and Yun Fu · 2023
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Partial network cloning
Jingwen Ye, Songhua Liu, and Xinchao Wang · 2023
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Dataset distillation: A comprehensive review
Ruonan Yu, Songhua Liu, and Xinchao Wang · 2023
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