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Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models.
Sur la distance de deux lois de probabilité
Maurice Fréchet · 1957
Earlier work this paper cites.
Information Theoretical Analysis of Multivariate Correlation
S. Watanabe · 1960
Earlier work this paper cites.
Divergence measures and message passing
Tom Minka et al · 2005
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Representation Learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models
Mathieu Aubry, Daniel Maturana, Alexei Efros, Bryan Russell, and Josef Sivic · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Generating images with perceptual similarity metrics based on deep networks
Alexey Dosovitskiy and Thomas Brox · 2016
Earlier work this paper cites.
ELBO surgery: yet another way to carve up the variational evidence lower bound
Matthew D. Hoffman and Matthew J. Johnson · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Earlier work this paper cites.
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
Earlier work this paper cites.
Adversarial autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Earlier work this paper cites.
Neural photo editing with introspective adversarial networks
Andrew Brock, Theodore Lim, James M. Ritchie, and Nick Weston · 2017
Earlier work this paper cites.
GANs trained by a two time-scale update rule converge to a nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Gnter, and Sepp Hochreiter · 2017
Earlier work this paper cites.
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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Fader networks:manipulating images by sliding attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic DENOYER, and Marc Aurelio Ranzato · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2017
Cited alongside, same era.
dSprites: Disentanglement testing Sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
Cited alongside, same era.
Learning disentangled representations with semi-supervised deep generative models
Siddharth Narayanaswamy, T. Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah Goodman, Pushmeet Kohli, Frank Wood, and Philip Torr · 2017
Cited alongside, same era.
Which training methods for gans do actually converge?
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2018
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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
Later among the works it cites.
Recent advances in autoencoder-based representation learning
Michael Tschannen, Olivier Frederic Bachem, and Mario Lučić · 2018
Later among the works it cites.
Improving the improved training of wasserstein GANs
Xiang Wei, Zixia Liu, Liqiang Wang, and Boqing Gong · 2018
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Weakly supervised disentanglement by pairwise similarities
Junxiang Chen and Kayhan Batmanghelich · 2019
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Flexibly fair representation learning by disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel · 2019
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Toward multimodal image-to-image translation
Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A Efros, Oliver Wang, and Eli Shechtman · 2017
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Information dropout: Learning optimal representations through noisy computation
A. Achille and S. Soatto · 2018
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Gilbo: One metric to measure them all
Alexander A Alemi and Ian Fischer · 2018
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Understanding disentangling in
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 variational autoencoders
Tian Qi Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik · 2018
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Introvae: Introspective variational autoencoders for photographic image synthesis
Huaibo Huang, zhihang li, Ran He, Zhenan Sun, and Tieniu Tan · 2018
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IB-GAN: Disentangled representation learning with information bottleneck GAN, 2019
Insu Jeon, Wonkwang Lee, and Gunhee Kim · 2019
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Variational autoencoders and nonlinear ica: A unifying framework
Ilyes Khemakhem, Diederik Kingma, and Aapo Hyvärinen · 2019
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Overcoming the disentanglement vs reconstruction trade-off via jacobian supervision
José Lezama · 2019
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Infogan-cr: Disentangling generative adversarial networks with contrastive regularizers
Zinan Lin, Kiran Koshy Thekumparampil, Giulia C. Fanti, and Sewoong Oh · 2019
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OOGAN: disentangling GAN with one-hot sampling and orthogonal regularization
Bingchen Liu, Yizhe Zhu, Zuohui Fu, Gerard de Melo, and Ahmed Elgammal · 2019
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On the fairness of disentangled representations
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, and Olivier Bachem · 2019
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Disentangling factors of variation using few labels
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, and Olivier Bachem · 2019
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Hologan: Unsupervised learning of 3d representations from natural images
Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang · 2019
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Variational discriminator bottleneck: Improving imitation learning, inverse rl, and gans by constraining information flow
Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, and Sergey Levine · 2019
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Learning disentangled representations with reference-based variational autoencoders
Adria Ruiz, Oriol Martínez, Xavier Binefa, and Jakob Verbeek · 2019
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Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
Nicholas Watters, Loïc Matthey, Christopher P. Burgess, and Alexander Lerchner · 2019
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