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In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
Texture synthesis by non-parametric sampling
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A parametric texture model based on joint statistics of complex wavelet coefficients
Portilla, Javier and Simoncelli, Eero P · 2000
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Example-based super-resolution
Freeman, William T, Jones, Thouis R, and Pasztor, Egon C · 2002
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Training invariant support vector machines using selective sampling
Loosli, Gaëlle, Canu, Stéphane, and Bottou, Léon · 2006
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Scene completion using millions of photographs
Hays, James and Efros, Alexei A · 2007
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Imagenet: A large-scale hierarchical image database
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li · 2009
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Lee, Honglak, Grosse, Roger, Ranganath, Rajesh, and Ng, Andrew Y · 2009
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Rectified linear units improve restricted boltzmann machines
Nair, Vinod and Hinton, Geoffrey E · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, Pascal, Larochelle, Hugo, Lajoie, Isabelle, Bengio, Yoshua, and Manzagol, Pierre-Antoine · 2010
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Selecting receptive fields in deep networks
Coates, Adam and Ng, Andrew · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Yuval, Wang, Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y · 2011
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Random search for hyper-parameter optimization
Bergstra, James and Bengio, Yoshua · 2012
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Learning feature representations with k-means
Coates, Adam and Ng, Andrew Y · 2012
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Goodfellow, Ian J, Warde-Farley, David, Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua · 2013
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Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2013
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Rectifier nonlinearities improve neural network acoustic models
Maas, Andrew L, Hannun, Awni Y, and Ng, Andrew Y · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, Tomas, Sutskever, Ilya, Chen, Kai, Corrado, Greg S, and Dean, Jeff · 2013
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Learning to generate chairs with convolutional neural networks
Dosovitskiy, Alexey, Springenberg, Jost Tobias, and Brox, Thomas · 2014
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
Dosovitskiy, Alexey, Fischer, Philipp, Springenberg, Jost Tobias, Riedmiller, Martin, and Brox, Thomas · 2015
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Draw: A recurrent neural network for image generation
Gregor, Karol, Danihelka, Ivo, Graves, Alex, and Wierstra, Daan · 2015
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Train faster, generalize better: Stability of stochastic gradient descent
Hardt, Moritz, Recht, Benjamin, and Singer, Yoram · 2015
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Dreaming more data: Class-dependent distributions over diffeomorphisms for learned data augmentation
Hauberg, Søren, Freifeld, Oren, Larsen, Anders Boesen Lindbo, Fisher III, John W., and Hansen, Lars Kair · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Generative adversarial nets
Goodfellow, Ian J., Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron C., and Bengio, Yoshua · 2014
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Adam: A method for stochastic optimization
Kingma, Diederik P and Ba, Jimmy Lei · 2014
Cited alongside, same era.
Learning and transferring mid-level image representations using convolutional neural networks
Oquab, M., Bottou, L., Laptev, I., and Sivic, J · 2014
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Striving for simplicity: The all convolutional net
Springenberg, Jost Tobias, Dosovitskiy, Alexey, Brox, Thomas, and Riedmiller, Martin · 2014
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Understanding locally competitive networks
Srivastava, Rupesh Kumar, Masci, Jonathan, Gomez, Faustino, and Schmidhuber, Jürgen · 2014
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Visualizing and understanding convolutional networks
Zeiler, Matthew D and Fergus, Rob · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
Denton, Emily, Chintala, Soumith, Szlam, Arthur, and Fergus, Rob · 2015
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Ioffe, Sergey and Szegedy, Christian · 2015
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Inceptionism : Going deeper into neural networks
Mordvintsev, Alexander, Olah, Christopher, and Tyka, Mike · 2015
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Semi-supervised learning with ladder network
Rasmus, Antti, Valpola, Harri, Honkala, Mikko, Berglund, Mathias, and Raiko, Tapani · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, Jascha, Weiss, Eric A, Maheswaranathan, Niru, and Ganguli, Surya · 2015
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A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M · 2015
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Empirical evaluation of rectified activations in convolutional network
Xu, Bing, Wang, Naiyan, Chen, Tianqi, and Li, Mu · 2015
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Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, Fisher, Zhang, Yinda, Song, Shuran, Seff, Ari, and Xiao, Jianxiong · 2015
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Stacked what-where auto-encoders
Zhao, Junbo, Mathieu, Michael, Goroshin, Ross, and Lecun, Yann · 2015
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