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Disentangled representation learning is one of the major goals of deep learning, and is a key step for achieving explainable and generalizable models.
Der endlichkeitssatz der invarianten endlicher gruppen
Emmy Noether · 1915
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Der endlichkeitssatz der invarianten endlicher gruppen
Emmy Noether · 1915
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Information theoretical analysis of multivariate correlation
Satosi Watanabe · 1960
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Information theoretical analysis of multivariate correlation
Satosi Watanabe · 1960
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More is different
Philip W Anderson · 1972
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More is different
Philip W Anderson · 1972
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Symmetry groups and their applications
Willard Miller · 1973
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Symmetry groups and their applications
Willard Miller · 1973
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Abstract algebra , volume 1999
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Abstract algebra , volume 1999
David S Dummit and Richard M Foote · 1991
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Violin plots: a box plot-density trace synergism
Jerry L Hintze and Ray D Nelson · 1998
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Violin plots: a box plot-density trace synergism
Jerry L Hintze and Ray D Nelson · 1998
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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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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 matching under the bregman divergence: a unified framework of density-ratio estimation
Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori · 2012
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Density-ratio matching under the bregman divergence: a unified framework of density-ratio estimation
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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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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Generative adversarial networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Generative adversarial networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Lie groups, Lie algebras, and representations: an elementary introduction , volume 222
Brian Hall · 2015
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Deep visual analogy-making
Scott E. Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 2015
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Lie groups, Lie algebras, and representations: an elementary introduction , volume 222
Brian Hall · 2015
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Deep visual analogy-making
Scott E. Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 2015
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
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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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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
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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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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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Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2017
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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 · 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
Cited alongside, same era.
Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2017
Bayes-factor-vae: Hierarchical bayesian deep auto-encoder models for factor disentanglement
Minyoung Kim, Yuting Wang, Pritish Sahu, and Vladimir Pavlovic · 2019
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Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation
Yu-Jhe Li, Ci-Siang Lin, Yan-Bo Lin, and Yu-Chiang Frank Wang · 2019
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Disentangling disentanglement in variational autoencoders
Emile Mathieu, Tom Rainforth, N Siddharth, and Yee Whye Teh · 2019
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Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness
Raphael Suter, Djordje Miladinovic, Bernhard Schölkopf, and Stefan Bauer · 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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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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Understanding disentangling in b e t a beta -vae
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
Ricky TQ Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 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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Towards a definition of disentangled representations
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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Overcoming the disentanglement vs reconstruction trade-off via jacobian supervision
José Lezama · 2018
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Abstract algebra: theory and applications
Thomas W Judson · 2020
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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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High-fidelity synthesis with disentangled representation
Wonkwang Lee, Donggyun Kim, Seunghoon Hong, and Honglak Lee · 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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Linear disentangled representations and unsupervised action estimation
Matthew Painter, Adam Prugel-Bennett, and Jonathon Hare · 2020
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Disentangling by subspace diffusion
David Pfau, Irina Higgins, Alex Botev, and Sébastien Racanière · 2020
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Learning group structure and disentangled representations of dynamical environments
Robin Quessard, Thomas D Barrett, and William R Clements · 2020
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Controlvae: Controllable variational autoencoder
Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, and Tarek Abdelzaher · 2020
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Improving the reconstruction of disentangled representation learners via multi-stage modelling
Akash Srivastava, Yamini Bansal, Yukun Ding, Cole Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D Cox, and Dan Gutfreund · 2020
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Joint disentangling and adaptation for cross-domain person re-identification
Yang Zou, Xiaodong Yang, Zhiding Yu, BVK Kumar, and Jan Kautz · 2020
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Abstract algebra: theory and applications
Thomas W Judson · 2020
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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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High-fidelity synthesis with disentangled representation
Wonkwang Lee, Donggyun Kim, Seunghoon Hong, and Honglak Lee · 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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Linear disentangled representations and unsupervised action estimation
Matthew Painter, Adam Prugel-Bennett, and Jonathon Hare · 2020
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Disentangling by subspace diffusion
David Pfau, Irina Higgins, Alex Botev, and Sébastien Racanière · 2020
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Learning group structure and disentangled representations of dynamical environments
Robin Quessard, Thomas D Barrett, and William R Clements · 2020
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Controlvae: Controllable variational autoencoder
Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, and Tarek Abdelzaher · 2020
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Improving the reconstruction of disentangled representation learners via multi-stage modelling
Akash Srivastava, Yamini Bansal, Yukun Ding, Cole Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D Cox, and Dan Gutfreund · 2020
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Joint disentangling and adaptation for cross-domain person re-identification
Yang Zou, Xiaodong Yang, Zhiding Yu, BVK Kumar, and Jan Kautz · 2020
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Addressing the topological defects of disentanglement, 2021
Stephane Deny Diane Bouchacourt, Mark Ibrahim · 2021
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On disentangled representations extracted from pretrained gans, 2021
Valentin Khrulkov, Leyla Mirvakhabova, Ivan Oseledets, and Artem Babenko · 2021
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Addressing the topological defects of disentanglement, 2021
Stephane Deny Diane Bouchacourt, Mark Ibrahim · 2021
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On disentangled representations extracted from pretrained gans, 2021
Valentin Khrulkov, Leyla Mirvakhabova, Ivan Oseledets, and Artem Babenko · 2021
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