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We address the problem of unsupervised disentanglement of discrete and continuous explanatory factors of data.
Statistical theory of extreme values and some practical applications
Gumbel, E. J · 1954
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Information theoretical analysis of multivariate correlation
Watanabe, S · 1960
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Learning factorial codes by predictability minimization
Schmidhuber, J · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
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Auto-encoding variational bayes, 2013
Kingma, D. P. and Welling, M · 2013
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Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models
Aubry, M., Maturana, D., Efros, A. A., Russell, B. C., and Sivic, J · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Neural variational inference and learning in belief networks
Mnih, A. and Gregor, K · 2014
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Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Neural discrete representation learning
van den Oord, A., Vinyals, O., et al · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Understanding disentangling in β \beta -vae
Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A · 2018
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Isolating sources of disentanglement in variational autoencoders
Chen, T. Q., Li, X., Grosse, R. B., and Duvenaud, D. K · 2018
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Learning disentangled joint continuous and discrete representations
Dupont, E · 2018
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Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
Cited alongside, same era.
Variational inference for monte carlo objectives
Mnih, A. and Rezende, D. J · 2016
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
Cited alongside, same era.
dsprites: Disentanglement testing sprites dataset
Matthey, L., Higgins, I., Hassabis, D., and Lerchner, A · 2017
Cited alongside, same era.
Gao, S., Brekelmans, R., Steeg, G. V., and Galstyan, A · 2018
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Efficient end-to-end learning for quantizable representations
Jeong, Y. and Song, H. O · 2018
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Disentangling by factorising
Kim, H. and Mnih, A · 2018
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