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In this paper we investigate image classification with computational resource limits at test time.
Optimal brain damage
Yann LeCun, John S Denker, Sara A Solla, Richard E Howard, and Lawrence D Jackel · 1989
Earlier work this paper cites.
Optimal brain surgeon and general network pruning
Babak Hassibi, David G Stork, and Gregory J Wolff · 1993
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Robust real-time object detection
Paul Viola and Michael Jones · 2001
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Model compression
Cristian Bucilua, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
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 and Geoffrey Hinton · 2009
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Speedboost: Anytime prediction with uniform near-optimality
Alexander Grubb and Drew Bagnell · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
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Zhixiang Xu, Olivier Chapelle, and Kilian Q. Weinberger · 2012
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Kirill Trapeznikov and Venkatesh Saligrama · 2013
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Cost-sensitive tree of classifiers
Zhixiang Xu, Matt Kusner, Minmin Chen, and Kilian Q. Weinberger · 2013
Earlier work this paper cites.
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Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2014
Earlier work this paper cites.
Anytime recognition of objects and scenes
Sergey Karayev, Mario Fritz, and Trevor Darrell · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep learning for content-based image retrieval: A comprehensive study
Ji Wan, Dayong Wang, Steven Chu Hong Hoi, Pengcheng Wu, Jianke Zhu, Yongdong Zhang, and Jintao Li · 2014
Earlier work this paper cites.
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Sergey Ioffe and Christian Szegedy · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger · 2016
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Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Tsung-Wei Ke, Michael Maire, and Stella X. Yu · 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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Convolutional neural fabrics
Shreyas Saxena and Jakob Verbeek · 2016
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