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The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.
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Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Unsupervised feature extraction by time-contrastive learning and nonlinear ica
Aapo Hyvarinen and Hiroshi Morioka · 2016
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Disentangling factors of variation in deep representation using adversarial training
Michael F Mathieu, Junbo J Zhao, Aditya Ramesh, Pablo Sprechmann, and Yann LeCun · 2016
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William F Whitney, Michael Chang, Tejas Kulkarni, and Joshua B Tenenbaum · 2016
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Factorized variational autoencoders for modeling audience reactions to movies
Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, and Greg Mori · 2017
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Karl Ridgeway and Michael C Mozer · 2018
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Rajen D Shah and Jonas Peters · 2018
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Improving generalization for abstract reasoning tasks using disentangled feature representations
Xander Steenbrugge, Sam Leroux, Tim Verbelen, and Bart Dhoedt · 2018
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Michael Tschannen, Olivier Bachem, and Mario Lucic · 2018
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A heuristic for unsupervised model selection for variational disentangled representation learning
Sunny Duan, Nicholas Watters, Loic Matthey, Christopher P Burgess, Alexander Lerchner, and Irina Higgins · 2019
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On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Muhammad Waleed Gondal, Manuel Wüthrich, Djordje Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 2019
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Group-based learning of disentangled representations with generalizability for novel contents
Haruo Hosoya · 2019
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Aapo Hyvarinen, Hiroaki Sasaki, and Richard E Turner · 2019
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On the fairness of disentangled representations
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Variational autoencoders recover pca directions (by accident)
Michal Rolinek, Dominik Zietlow, and Georg Martius · 2019
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Interventional robustness of deep latent variable models
Raphael Suter, Djordje Miladinović, Stefan Bauer, and Bernhard Schölkopf · 2019
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Are disentangled representations helpful for abstract visual reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem · 2019
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Variational autoencoders and nonlinear ica: A unifying framework
Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, and Aapo Hyvarinen · 2020
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Weakly supervised disentanglement with guarantees
Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Disentanglement by nonlinear ica with general incompressible-flow networks (gin)
Peter Sorrenson, Carsten Rother, and Ullrich Köthe · 2020
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