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Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network.
Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
Ludmila I. Kuncheva and Christopher J. Whitaker · 2003
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Model compression
Cristian Bucila, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Cifar-10 (canadian institute for advanced research), 2009
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
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Do deep nets really need to be deep?
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Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Learning using privileged information: Similarity control and knowledge transfer
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Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
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End-to-end comparative attention networks for person re-identification
Hao Liu, Jiashi Feng, Meibin Qi, Jianguo Jiang, and Shuicheng Yan · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Entropy-sgd: Biasing gradient descent into wide valleys
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Regularizing neural networks by penalizing confident output distributions
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Unifying distillation and privileged information
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Xnor-net: Imagenet classification using binary convolutional neural networks
M Rastegari, V Ordonez, J Redmon, and A Farhadi · 2016
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
William J. Dally Song Han, Huizi Mao · 2016
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Dual learning for machine translation
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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Gated siamese convolutional neural network architecture for human re-identification
Rahul Rama Varior, Mrinal Haloi, and Gang Wang
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A siamese long short-term memory architecture for human re-identification
Rahul Rama Varior, Bing Shuai, Jiwen Lu, Dong Xu, and Gang Wang
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Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey Hinton · 2017
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Understanding deep learning requires rethinking generalization
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Re-ranking person re-identification with k k -reciprocal encoding
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