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Recent research has shown that generative models with highly disentangled representations fail to generalise to unseen combination of generative factor values.
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
Nicholas Watters, Loic Matthey, Christopher P. Burgess, and Alexander Lerchner · 1901
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Unsupervised Model Selection for Variational Disentangled Representation Learning
Sunny Duan, Loic Matthey, Andre Saraiva, Nicholas Watters, Christopher P. Burgess, Alexander Lerchner, and Irina Higgins · 1905
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Algorithms for the assignment and transportation problems
James Munkres · 1957
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Connectionism, constituency, and the language of thought
Paul Smolensky · 1988
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Humboldt:’On language’: On the diversity of human language construction and its influence on the mental development of the human species
Wilhelm Von Humboldt, Wilhelm Freiherr von Humboldt, et al · 1999
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Matplotlib: A 2d graphics environment
J. D. Hunter · 2007
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2013
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Aspects of the Theory of Syntax , volume 11
Noam Chomsky · 2014
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Unsupervised feature extraction by time-contrastive learning and nonlinear ica
Aapo Hyvarinen and Hiroshi Morioka · 2016
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β \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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Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2017
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dSprites: Disentanglement testing Sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2017
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The Sacred Infrastructure for Computational Research
Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, and Jürgen Schmidhuber · 2017
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Understanding disentangling in $\beta$-VAE
Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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3d shapes dataset
Chris Burgess and Hyunjik Kim · 2018
Cited alongside, same era.
Weakly-supervised disentanglement without compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen · 2020
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Infogan-cr and modelcentrality: Self-supervised model training and selection for disentangling gans
Zinan Lin, Kiran Thekumparampil, Giulia Fanti, and Sewoong Oh · 2020
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Towards nonlinear disentanglement in natural data with temporal sparse coding
David Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, and Dylan Paiton · 2020
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The role of disentanglement in generalisation
Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, and Jeffrey Bowers · 2020
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
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A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher KI Williams · 2018
Cited alongside, same era.
Hyunjik Kim and Andriy Mnih · 2019
Cited alongside, same era.
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Sjoerd van Steenkiste, Jürgen Schmidhuber, Francesco Locatello, and Olivier Bachem · 2019
Cited alongside, same era.
On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 2019
Cited alongside, same era.
Learning discrete and continuous factors of data via alternating disentanglement
Yeonwoo Jeong and Hyun Oh Song · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Irina Higgins, Arka Pal, Andrei A. Rusu, Loic Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, and Alexander Lerchner
Cited in the paper.
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High-level library to help with training neural networks in pytorch
V. Fomin, J. Anmol, S. Desroziers, Kriss J., and A. Tejani · 2020
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Toward causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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Infinite use of finite means? evaluating the generalization of center embedding learned from an artificial grammar
R Thomas McCoy, Jennifer Culbertson, Paul Smolensky, and Géraldine Legendre · 2021
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Visual representation learning does not generalize strongly within the same domain
Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland Brendel · 2021
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Commutative Lie Group VAE for Disentanglement Learning
Xinqi Zhu, Chang Xu, and Dacheng Tao · 2021
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seaborn: statistical data visualization
Michael L. Waskom · 2021
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