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Structural pruning can simplify network architecture and improve inference speed.
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cudnn: Efficient primitives for deep learning
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Deep residual learning for image recognition. corr abs/1512.03385 (2015), 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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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, Alexander C. Berg, and Li Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Jose M Alvarez and Mathieu Salzmann · 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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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 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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Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 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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Speed/accuracy trade-offs for modern convolutional object detectors
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Theoretical analysis of self-training with deep networks on unlabeled data
Ilya Loshchilov and Frank Hutter · 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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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Constraint-aware deep neural network compression
Changan Chen, Frederick Tung, Naveen Vedula, and Greg Mori · 2018
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Dpp-net: Device-aware progressive search for pareto-optimal neural architectures
Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, and Min Sun · 2018
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Dynamic channel pruning: Feature boosting and suppression
Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, and Cheng-zhong Xu · 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
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Iamnn: Iterative and adaptive mobile neural network for efficient image classification
Sam Leroux, Pavlo Molchanov, Pieter Simoens, Bart Dhoedt, Thomas Breuel, and Jan Kautz · 2018
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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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Autoslim: Towards one-shot architecture search for channel numbers
Jiahui Yu and Thomas Huang · 2019
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Knapsack pruning with inner distillation
Yonathan Aflalo, Asaf Noy, Ming Lin, Itamar Friedman, and Lihi Zelnik · 2020
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Language models are few-shot learners
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Zeroq: A novel zero shot quantization framework
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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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
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ChamNet: Towards efficient network design through platform-aware model adaptation
Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, P Vajda, M Uyttendaele, and Niraj K Jha · 2019
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Centripetal sgd for pruning very deep convolutional networks with complicated structure
Xiaohan Ding, Guiguang Ding, Yuchen Guo, and Jungong Han · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 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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Progressive skeletonization: Trimming more fat from a network at initialization
Pau de Jorge, Amartya Sanyal, Harkirat S Behl, Philip HS Torr, Gregory Rogez, and Puneet K Dokania · 2020
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Eagleeye: Fast sub-net evaluation for efficient neural network pruning
Bailin Li, Bowen Wu, Jiang Su, and Guangrun Wang · 2020
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Neural network pruning with residual-connections and limited-data
Jian-Hao Luo and Jianxin Wu · 2020
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Convolutional networks for image classification in pytorch
Nvidia · 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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Unas: Differentiable architecture search meets reinforcement learning
Arash Vahdat, Arun Mallya, Ming-Yu Liu, and Jan Kautz · 2020
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Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
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Apq: Joint search for network architecture, pruning and quantization policy
Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Hanrui Wang, Yujun Lin, and Song Han · 2020
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Constraint-aware importance estimation for global filter pruning under multiple resource constraints
Yu-Cheng Wu, Chih-Ting Liu, Bo-Ying Chen, and Shao-Yi Chien · 2020
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Dreaming to distill: Data-free knowledge transfer via DeepInversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 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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Chip: Channel independence-based pruning for compact neural networks
Yang Sui, Miao Yin, Yi Xie, Huy Phan, Saman Aliari Zonouz, and Bo Yuan · 2021
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Chipnet: Budget-aware pruning with heaviside continuous approximations
Rishabh Tiwari, Udbhav Bamba, Arnav Chavan, and Deepak Gupta · 2021
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Neural pruning via growing regularization
Huan Wang, Can Qin, Yulun Zhang, and Yun Fu · 2021
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Fully dynamic inference with deep neural networks
Wenhan Xia, Hongxu Yin, Xiaoliang Dai, and Niraj K Jha · 2021
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Optimal channel selection with discrete qcqp
Yeonwoo Jeong, Deokjae Lee, Gaon An, Changyong Son, and Hyun Oh Song · 2022
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When to prune? a policy towards early structural pruning
Maying Shen, Pavlo Molchanov, Hongxu Yin, and Jose M Alvarez · 2022
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A-ViT: Adaptive tokens for efficient vision transformer
Hongxu Yin, Arash Vahdat, Jose M Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov · 2022
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