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Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision.
Extracting slow subspaces from natural videos leads to complex cells
Christoph Kayser, Wolfgang Einhauser, Olaf Dummer, Peter Konig, and Konrad Kding · 2001
Earlier work this paper cites.
Slow feature analysis: Unsupervised learning of invariances
Laurenz Wiskott and Terrence J. Sejnowski · 2002
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Sparse coding of sensory inputs
Bruno A Olshausen and David J Field · 2004
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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
Earlier work this paper cites.
Deep learning from temporal coherence in video
Hossein Mobahi, Ronana Collobert, and Jason Weston · 2009
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Deconvolutional networks
Matthew D Zeiler, Dilip Krishnan, Graham W Taylor, and Robert Fergus · 2010
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Transforming auto-encoders
Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang · 2011
Cited alongside, same era.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron C. Courville, and Pascal Vincent · 2012
Cited alongside, same era.
Learning intermediate-level representations of form and motion from natural movies
Charles F. Cadieu and Bruno A. Olshausen · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Transformation properties of learned visual representations
Taco S Cohen and Max Welling · 2014
Cited alongside, same era.
Unsupervised learning of spatiotemporally coherent metrics
Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, and Yann LeCun · 2014
Later among the works it cites.
Learning and transferring mid-level image representations using convolutional neural networks
Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic · 2014
Later among the works it cites.
Video (language) modeling: a baseline for generative models of natural videos
MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra · 2014
Later among the works it cites.
Anticipating the future by watching unlabeled video
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2015
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Unsupervised learning of visual representations using videos
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Xiaolong Wang and Abhinav Gupta · 2015
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