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We present a novel architecture, the "stacked what-where auto-encoders" (SWWAE), which integrates discriminative and generative pathways and provides a unified approach to supervised, semi-supervised and unsupervised learning without relying on sampling during training.
Gradient-based learning applied to document recognition
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Vladimir Naumovich Vapnik and Vlamimir Vapnik · 1998
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Geoffrey Hinton, Simon Osindero, and Yee-Whye Teh · 2006
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A sparse and locally shift invariant feature extractor applied to document images
M Ranzato and Yann LeCun · 2007
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Unsupervised learning of invariant feature hierarchies with applications to object recognition
M Ranzato, Fu Jie Huang, Y-L Boureau, and Yann LeCun · 2007
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Fast inference in sparse coding algorithms with applications to object recognition
Koray Kavukcuoglu, Marc’Aurelio Ranzato, and Yann LeCun · 2008
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Classification using discriminative restricted boltzmann machines
Hugo Larochelle and Yoshua Bengio · 2008
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Semi-supervised learning of compact document representations with deep networks
Marc’Aurelio Ranzato and Martin Szummer · 2008
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80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 2008
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Learning invariant features through topographic filter maps
Koray Kavukcuoglu, Marc’Aurelio Ranzato, Rob Fergus, and Yann LeCun · 2009
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Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
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Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
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Learning convolutional feature hierachies for visual recognition
Koray Kavukcuoglu, Pierre Sermanet, Y-Lan Boureau, Karol Gregor, Michaël Mathieu, and Yann LeCun · 2010
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Deconvolutional networks
Matthew D Zeiler, Dilip Krishnan, Graham W Taylor, and Robert Fergus · 2010
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Unsupervised learning of sparse features for scalable audio classification
Mikael Henaff, Kevin Jarrett, Koray Kavukcuoglu, and Yann LeCun · 2011
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Transforming auto-encoders
Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang · 2011
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Stacked convolutional auto-encoders for hierarchical feature extraction
Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber · 2011
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
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Benjamin Graham · 2014
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Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2014
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A winner-take-all method for training sparse convolutional autoencoders
Alireza Makhzani and Brendan Frey · 2014
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Improving neural networks by preventing co-adaptation of feature detectors
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Jason Weston, Frédéric Ratle, Hossein Mobahi, and Ronan Collobert · 2012
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Dong-Hyun Lee · 2013
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Alireza Makhzani and Brendan Frey · 2013
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Multi-task bayesian optimization
Kevin Swersky, Jasper Snoek, and Ryan P Adams · 2013
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Semi-supervised learning with ladder network
Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, and Tapani Raiko
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An analysis of unsupervised pre-training in light of recent advances
Tom Le Paine, Pooya Khorrami, Wei Han, and Thomas S Huang · 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 Riedmiller · 2014
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Learning to linearize under uncertainty
Ross Goroshin, Michael Mathieu, and Yann LeCun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
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