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Filter pruning is one of the most effective ways to accelerate and compress convolutional neural networks (CNNs).
Optimal brain damage
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The PASCAL Visual Object Classes Challenge 2011 (VOC2011) Results
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The Caltech-UCSD Birds-200-2011 Dataset
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily L. Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
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Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J. Dally · 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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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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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
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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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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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Bharat Singh, Mahyar Najibi, and Larry S. Davis · 2018
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Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
Jianbo Ye, Xin Lu, Zhe Lin, and James Z. Wang · 2018
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Learning compact recurrent neural networks with block-term tensor decomposition
Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, and Zenglin Xu · 2018
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Bisenet: Bilateral segmentation network for real-time semantic segmentation
Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang · 2018
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NISP: pruning networks using neuron importance score propagation
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Towards effective low-bitwidth convolutional neural networks
Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, and Ian D. Reid · 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 Jin-Hui Zhu · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2018
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Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution
Yunpeng Chen, Haoqi Fan, Bing Xu, Zhicheng Yan, Yannis Kalantidis, Marcus Rohrbach, Shuicheng Yan, and Jiashi Feng · 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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Pruning filter via geometric median for deep convolutional neural networks acceleration
Yang He, Ping Liu, Ziwei Wang, and Yi Yang · 2019
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DARTS: differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Rethinking the value of network pruning
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2019
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Hetconv: Heterogeneous kernel-based convolutions for deep cnns
Pravendra Singh, Vinay Kumar Verma, Piyush Rai, and Vinay P. Namboodiri · 2019
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SNAS: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
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