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Learning rich and diverse representations is critical for the performance of deep convolutional neural networks (CNNs).
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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A new learning paradigm: Learning using privileged information
Vladimir Vapnik and Akshay Vashist · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Fast optimization algorithms for solving svm+
Dmitry Pechyony and Vladimir Vapnik · 2011
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Contextualizing object detection and classification
Zheng Song, Qiang Chen, Zhongyang Huang, Yang Hua, and Shuicheng Yan · 2011
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Object-centric spatial pooling for image classification
Olga Russakovsky, Yuanqing Lin, Kai Yu, and Li Fei-Fei · 2012
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Maxout networks
Ian J Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C Courville, and Yoshua Bengio · 2013
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Deep learning using linear support vector machines
Yichuan Tang · 2013
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Object localization based on structural svm using privileged information
Jan Feyereisl, Suha Kwak, Jeany Son, and Bohyung Han · 2014
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Simultaneous detection and segmentation
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2014
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Learning using privileged information: Svm+ and weighted svm
Maksim Lapin, Matthias Hein, and Bernt Schiele · 2014
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Learning to transfer privileged information
Viktoriia Sharmanska, Novi Quadrianto, and Christoph H Lampert · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Deep learning with s-shaped rectified linear activation units
Xiaojie Jin, Chunyan Xu, Jiashi Feng, Yunchao Wei, Junjun Xiong, and Shuicheng Yan · 2015
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Learning using privileged information: similarity control and knowledge transfer
Vladimir Vapnik and Rauf Izmailov · 2015
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Reducing overfitting in deep networks by decorrelating representations
Michael Cogswell, Faruk Ahmed, Ross Girshick, Larry Zitnick, and Dhruv Batra · 2016
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Unifying distillation and privileged information
David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, and Vladimir Vapnik · 2016
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Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang · 2015
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Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
Jifeng Dai, Kaiming He, and Jian Sun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Geometric Optimization in Machine Learning
Suvrit Sra and Reshad Hosseini · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alex Alemi · 2016
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Polynet: A pursuit of structural diversity in very deep networks
Xingcheng Zhang, Zhizhong Li, Chen Change Loy, and Dahua Lin · 2016
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