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We study the problem of building models that disentangle independent factors of variation.
Auto-association by multilayer perceptrons and singular value decomposition
Hervé Bourlard and Yves Kamp · 1988
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Transforming auto-encoders
Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang · 2011
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Discovering hidden factors of variation in deep networks
Brian Cheung, Jesse A Livezey, Arjun K Bansal, and Bruno A Olshausen · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Learning to disentangle factors of variation with manifold interaction
Scott Reed, Kihyuk Sohn, Yuting Zhang, and Honglak Lee · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Deep visual analogy-making
Scott E Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 2015
Coupled generative adversarial networks
Ming-Yu Liu and Oncel Tuzel · 2016
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2016
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Disentangling factors of variation in deep representation using adversarial training
Michael F Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, and Yann LeCun · 2016
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Neural face editing with intrinsic image disentangling
Zhixin Shu, Ersin Yumer, Sunil Hadap, Kalyan Sunkavalli, Eli Shechtman, and Dimitris Samaras · 2017
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Disentangled representation learning gan for pose-invariant face recognition
Luan Tran, Xi Yin, and Xiaoming Liu · 2017
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Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis
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Weakly-supervised disentangling with recurrent transformations for 3d view synthesis
Jimei Yang, Scott E Reed, Ming-Hsuan Yang, and Honglak Lee · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky · 2017
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Reconstruction for feature disentanglement in pose-invariant face recognition
Kihyuk Sohn Dimitris Metaxas Manmohan Chandraker Xi Peng, Xiang Yu · 2017
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