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In this paper, we are interested in building lightweight and efficient convolutional neural networks.
80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Robert Fergus, and William T. Freeman · 2008
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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 · 2009
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Simplifying convnets for fast learning
Franck Mamalet and Christophe Garcia · 2012
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
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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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Speeding up convolutional neural networks with low rank expansions
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller · 2014
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Training cnns with low-rank filters for efficient image classification
Yani Ioannou, Duncan P. Robertson, Jamie Shotton, Roberto Cipolla, and Antonio Criminisi · 2015
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Deeply-supervised nets
Chen-Yu Lee, Saining Xie, Patrick W. Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2015
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Training very deep networks
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
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Learning the number of neurons in deep networks
Jose M Alvarez and Mathieu Salzmann · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2016
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Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 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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Aggregated residual transformations for deep neural networks
Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2016
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Deep convolutional neural networks with merge-and-run mappings
Liming Zhao, Jingdong Wang, Xi Li, and Zhuowen Tu · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
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Gao Huang, Zhuang Liu, and Kilian Q. Weinberger · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Deep roots: Improving CNN efficiency with hierarchical filter groups
Yani Ioannou, Duncan P. Robertson, Roberto Cipolla, and Antonio Criminisi · 2016
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Fractalnet: Ultra-deep neural networks without residuals
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 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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Faster cnns with direct sparse convolutions and guided pruning
Jongsoo Park, Sheng Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, and Pradeep Dubey · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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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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Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
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Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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On the expressive power of overlapping architectures of deep learning
Or Sharir and Amnon Shashua · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen · 2017
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2017
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Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Igcv2: Interleaved structured sparse convolutional neural networks
Guotian Xie, Jingdong wang, Ting Zhang, Jianhuang Lai, Richang Hong, and Guo-Jun Qi · 2018
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