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Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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Matching pursuits with time-frequency dictionaries
Stephane Mallat and Zhifeng Zhang · 1993
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Bruno A Olshausen et al · 1996
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Unsupervised learning of invariant feature hierarchies with applications to object recognition
Marc Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau, and Yann LeCun · 2007
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Sparse feature learning for deep belief networks
Y-lan Boureau, Yann L Cun, et al · 2008
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The restricted isometry property and its implications for compressed sensing
Emmanuel J. Candés · 2008
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Sparse deep belief net model for visual area v2
Honglak Lee, Chaitanya Ekanadham, and Andrew Y Ng · 2008
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
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Iterative hard thresholding for compressed sensing
Thomas Blumensath and Mike E Davies · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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What is the best multi-stage architecture for object recognition?
Kevin Jarrett, Koray Kavukcuoglu, Marc’Aurelio Ranzato, and Yann LeCun · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Model-Based Compressive Sensing
R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde · 2010
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Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
Cited alongside, same era.
Image super-resolution via sparse representation
Jianchao Yang, John Wright, Thomas S Huang, and Yi Ma · 2010
Cited alongside, same era.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa · 2011
Cited alongside, same era.
Concentration of Measure for Block Diagonal Matrices With Applications to Compressive Signal Processing
Jae Young Park, Han Lun Yap, C.J. Rozell, and M. B. Wakin · 2011
Cited alongside, same era.
On random weights and unsupervised feature learning
Andrew Saxe, Pang W Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, and Andrew Y Ng · 2011
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Why does Deep Learning work? - A perspective from Group Theory
Arnab Paul and Suresh Venkatasubramanian · 2014
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Why are deep nets reversible: A simple theory, with implications for training
Sanjeev Arora, Yingyu Liang, and Tengyu Ma · 2015
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l 1 l1 -norm methods for convex-cardinality problems, ee364b: Convex optimization II lecture notes, 2014-2015 spring
Stephen Boyd · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Building high-level features using large scale unsupervised learning
Q. V. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. S. Corrado, J. Dean, and A. Y. Ng · 2013
Cited alongside, same era.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Cited alongside, same era.
Provable Bounds for Learning Some Deep Representations
Sanjeev Arora, Aditya Bhaskara, Rong Ge, and Tengyu Ma · 2014
Cited alongside, same era.
Signal recovery from pooling representations
Joan Bruna, Arthur Szlam, and Yann LeCun · 2014
Cited alongside, same era.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Cited alongside, same era.
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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Matconvnet – convolutional neural networks for matlab
A. Vedaldi and K. Lenc · 2015
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Inverting visual representations with convolutional networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Deep neural networks with random gaussian weights: A universal classification strategy?
Raja Giryes, Guillermo Sapiro, and Alex M Bronstein · 2016
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A powerful generative model using random weights for the deep image representation
Kun He, Yan Wang, and John Hopcroft · 2016
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Understanding and improving convolutional neural networks via concatenated rectified linear units
Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee · 2016
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Augmenting neural networks with reconstructive decoding pathways for large-scale image classification
Yuting Zhang, Kibok Lee, and Honglak Lee · 2016
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Stacked what-where auto-encoders
Junbo Zhao, Michael Mathieu, Ross Goroshin, and Yann Lecun · 2016
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