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Unsupervised learning of disentangled representations involves uncovering of different factors of variations that contribute to the data generation process.
Information theoretical analysis of multivariate correlation
Satosi Watanabe · 1960
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On the bootstrap of u and v statistics
Miguel A Arcones and Evarist Gine · 1992
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Learning factorial codes by predictability minimization
Jürgen Schmidhuber · 1992
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Topics in optimal transportation
Cédric Villani · 2003
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan · 2010
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Density ratio estimation in machine learning
Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori · 2012
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Adversarial autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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Deep visual analogy-making
Scott E Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 2015
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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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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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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Wasserstein variational inference
Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk, Max Hinne, Marcel AJ van Gerven, and Eric Maris · 2018
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Understanding disentangling in β \beta -vae
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
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Isolating sources of disentanglement in vaes
Ricky TQ Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
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Learning disentangled joint continuous and discrete representations
Emilien Dupont · 2018
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A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher KI Williams · 2018
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Unsupervised learning of disentangled representations from video
Emily L Denton et al · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Unsupervised learning of disentangled and interpretable representations from sequential data
Wei-Ning Hsu, Yu Zhang, and James Glass · 2017
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Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2017
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Emergence of invariance and disentanglement in deep representations
Alessandro Achille and Stefano Soatto · 2018
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Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, and Alexander Lerchner · 2018
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Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 2018
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Learning deep disentangled embeddings with the f-statistic loss
Karl Ridgeway and Michael C Mozer · 2018
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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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Wasserstein dependency measure for representation learning
Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, and Pierre Sermanet · 2019
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