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Disentangled encoding is an important step towards a better representation learning.
Information theoretical analysis of multivariate correlation
Satosi Watanabe · 1960
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Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
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Auto-encoding variational bayes, 2013
Diederik P Kingma and Max Welling · 2013
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Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman · 2016
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Variational inference of disentangled latent concepts from unlabeled observations, 2017
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2017
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Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Infovae: Information maximizing variational autoencoders, 2017
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
Cited alongside, same era.
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations , 2018
2018
Cited alongside, same era.
Understanding disentangling in β \beta -vae, 2018
Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders, 2018
Ricky T. Q. Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
Later among the works it cites.
β \beta -vaes can retain label information even at high compression, 2018
Emily Fertig, Aryan Arbabi, and Alexander A. Alemi · 2018
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Disentangling by factorising, 2018
Hyunjik Kim and Andriy Mnih · 2018
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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, Đorđe Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 2019
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