Fetching the paper…
Reading the bibliography…
Channel pruning is among the predominant approaches to compress deep neural networks.
An idea based on honey bee swarm for numerical optimization
Dervis Karaboga · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Efficient and accurate approximations of nonlinear convolutional networks
Xiangyu Zhang, Jianhua Zou, Xiang Ming, Kaiming He, and Jian Sun · 2015
Earlier work this paper cites.
Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang · 2016
Earlier work this paper cites.
Net-trim: Convex pruning of deep neural networks with performance guarantee
Alireza Aghasi, Afshin Abdi, Nam Nguyen, and Justin Romberg · 2017
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
Cited alongside, same era.
Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
Cited alongside, same era.
Runtime neural pruning
Ji Lin, Yongming Rao, Jiwen Lu, and Jie Zhou · 2017
Cited alongside, same era.
Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
Cited alongside, same era.
Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
Cited alongside, same era.
Data-driven sparse structure selection for deep neural networks
Zehao Huang and Naiyan Wang · 2018
Later among the works it cites.
Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference
Jian-Hao Luo and Jianxin Wu · 2018
Later among the works it cites.
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
Later among the works it cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Later among the works it cites.
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
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
Cited alongside, same era.
Constraint-aware deep neural network compression
Changan Chen, Frederick Tung, Naveen Vedula, and Greg Mori · 2018
Cited alongside, same era.
Soft filter pruning for accelerating deep convolutional neural networks
Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang · 2018
Cited alongside, same era.
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.
Metapruning: Meta learning for automatic neural network channel pruning
Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Tim Kwang-Ting Cheng, and Jian Sun · 2019
Later among the works it cites.
Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2019
Later among the works it cites.
Play and prune: Adaptive filter pruning for deep model compression
Pravendra Singh, Vinay Kumar Verma, Piyush Rai, and Vinay P. Namboodiri · 2019
Later among the works it cites.
Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
Later among the works it cites.
Hrank: Filter pruning using high-rank feature map
Mingbao Lin, Rongrong Ji, Yan Wang, Yichen Zhang, Baochang Zhang, Yonghong Tian, and Ling Shao · 2020
Closest in time.