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We propose a strong baseline model for unsupervised feature learning using video data.
Learning representations by back-propagating errors
Rumelhart, D.E., Hinton, G.E., and Williams, R.J · 1986
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Sparse coding with an overcomplete basis set: a strategy employed by v1?
Olshausen, B. A. and Field, D. J · 1997
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Natural images, gaussian mixtures and dead leaves
Zoran, D. and Weiss, Y · 1997
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Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex
van Hateren, J.H. and Ruderman, D.L · 1998
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Learning flexible sprites in video layers
Jojic, N. and Frey, B.J · 2001
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Slow feature analysis: unsupervised learning of invariances
Wiskott, L. and Sejnowski, T · 2002
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A neural probabilistic language model
Bengio, Y., Ducharme, R., Vincent, P., and Jauvin, C · 2003
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Simple-cell-like receptive fields maximize temporal coherence in natural video
Hurri, J. and Hyvärinen, A · 2003
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High accuracy optical flow estimation based on a theory for warping
Brox, T., Bruhn, A., Papenberg, N., and Weickert, J · 2004
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Reducing the dimensionality of data with neural networks
Hinton, G.E. and Salakhutdinov, R. R · 2006
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Sparse deep belief net model for visual area v2
Lee, H., Chaitanya, E., and Ng, A. Y · 2007
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Learning the lie groups of visual invariance
Miao, X. and Rao, R · 2007
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Fast inference in sparse coding algorithms with applications to object recognition
Kavukcuoglu, K., Ranzato, M., and LeCun, Y · 2008
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.A · 2008
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Learning transformational invariants from natural movies
Cadieu, C. and Olshausen, B · 2009
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Learning to represent spatial transformations with factored higher-order boltzmann machines
Memisevic, R. and Hinton, G.E · 2009
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Visual parsing after recovery from blindness
Ostrovsky, Y., Meyers, E., Ganesh, S., Mathur, U., and Sinha, P · 2009
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
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Learning visual motion in recurrent neural networks
Pachitariu, M. and Sahani, M · 2012
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Ucf101: A dataset of 101 human action classes from videos in the wild
Soomro, K., Zamir, A.R., and Shah, M · 2012
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Mixtures of conditional gaussian scale mixtures applied to multiscale image representations
Theis, L., Hosseini, R., and Bethge, M · 2012
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Modeling natural images using gated mrfs
Ranzato, M., Mnih, V., Susskind, J., and Hinton, G · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, Ross, Donahue, Jeff, Darrell, Trevor, and Malik, Jitendra · 2014
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The recurrent temporal restricted boltzmann machine
Sutskever, I., Hinton, G.E., and Taylor, G.W · 2009
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Emergence of complex-like cells in a temporal product network with local receptive fields
Gregor, K. and LeCun, Y · 2010
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Recurrent neural network based language model
Mikolov, T., Karafiat, M., Burget, L., Cernocky, J., and Khudanpur, S · 2010
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Two distributed-state models for generating high-dimensional time series
Taylor, G.W., Hinton, G.E., and Roweis, S. T · 2011
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In all likelihood, deep belief is not enough
Theis, L., Gerwinn, S., Sinz, F., and Bethge, M · 2011
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Generative adversarial nets
Goodfellow, I, Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Simultaneous detection and segmentation
Hariharan, B., Arbeláez, P., Girshick, R., and Malik, J · 2014
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Modeling deep temporal dependencies with recurrent ”grammar cells”
Michalski, V., Memisevic, R., and Konda, K · 2014
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Two-stream convolutional networks for action recognition in videos
Simonyan, K. and Zisserman, A · 2014
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