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We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time budgets during application.
Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce · 2006
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Multi-class active learning for image classification
Ajay J Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos · 2009
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Recognizing indoor scenes
Ariadna Quattoni and Antonio Torralba · 2009
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As time goes by: Anytime semantic segmentation with iterative context forests
Björn Fröhlich, Erik Rodner, and Joachim Denzler · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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The greedy miser: Learning under test-time budgets
Zhixiang Xu, Kilian Weinberger, and Olivier Chapelle · 2012
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2013
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Fast training of convolutional networks through ffts
Michael Mathieu, Mikael Henaff, and Yann LeCun · 2013
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Learning autonomous driving styles and maneuvers from expert demonstration
David Silver, J Andrew Bagnell, and Anthony Stentz · 2013
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Anytime representation learning
Zhixiang Xu, Matt Kusner, Gao Huang, and Kilian Q Weinberger · 2013
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Cited alongside, same era.
Convolutional neural networks at constrained time cost
Kaiming He and Jian Sun · 2014
Cited alongside, same era.
Speeding up convolutional neural networks with low rank expansions
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman · 2014
Cited alongside, same era.
Anytime recognition of objects and scenes
Sergey Karayev, Mario Fritz, and Trevor Darrell · 2014
Cited alongside, same era.
Fast convnets using group-wise brain damage
Vadim Lebedev and Victor Lempitsky · 2015
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2015
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The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification
Lingqiao Liu, Chunhua Shen, and Anton van den Hengel · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Training deeper convolutional networks with deep supervision
Liwei Wang, Chen-Yu Lee, Zhuowen Tu, and Svetlana Lazebnik · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Learning deep features for scene recognition using places database
Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Aude Oliva · 2014
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Fast algorithms for convolutional neural networks
Andrew Lavin · 2015
Cited alongside, same era.
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Learning deep representation for face alignment with auxiliary attributes
Z. Zhang, P. Luo, C. C. Loy, and X. Tang · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Deep spatial autoencoders for visuomotor learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel · 2016
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Locally-supervised deep hybrid model for scene recognition
Sheng Guo, Weilin Huang, and Yu Qiao · 2016
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