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Searching for network width is an effective way to slim deep neural networks with hardware budgets.
Genetic algorithms and neural networks: Optimizing connections and connectivity
Darrell Whitley, Timothy Starkweather, and Christopher Bogart · 1990
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Peter JB Hancock · 1992
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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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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher K I Williams, John Winn, and Andrew Zisserman · 2010
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The cifar-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
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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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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep learning face attributes in the wild
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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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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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More is less: A more complicated network with less inference complexity
Xuanyi Dong, Junshi Huang, Yi Yang, and Shuicheng Yan · 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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Multi-scale dense networks for resource efficient image classification
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, 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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Learning from multiple teacher networks
Shan You, Chang Xu, Chao Xu, and Dacheng Tao · 2017
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Dynamic channel pruning: Feature boosting and suppression
Xitong Gao, Yiren Zhao, Lukasz Dudziak, Robert Mullins, and Cheng-zhong Xu · 2018
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Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Multi-task learning as multi-objective optimization
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Bringing giant neural networks down to earth with unlabeled data
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Pruning from scratch
Yulong Wang, Xiaolu Zhang, Lingxi Xie, Jun Zhou, Hang Su, Bo Zhang, and Xiaolin Hu · 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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Autoslim: Towards one-shot architecture search for channel numbers
Jiahui Yu and Thomas Huang · 2019
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Network adjustment: Channel search guided by flops utilization ratio
Zhengsu Chen, Jianwei Niu, Lingxi Xie, Xuefeng Liu, Longhui Wei, and Qi Tian · 2020
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Legr: Filter pruning via learned global ranking
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Approximated oracle filter pruning for destructive cnn width optimization
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Network pruning via transformable architecture search
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Channel gating neural networks
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Yibo Hu, Xiang Wu, and Ran He · 2020
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Explicitly learning topology for differentiable neural architecture search
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Learning student networks with few data
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Data agnostic filter gating for efficient deep networks
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Good subnetworks provably exist: Pruning via greedy forward selection
Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R Klivans, and Qiang Liu · 2020
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Greedynas: Towards fast one-shot nas with greedy supernet
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