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We propose a novel VAE-based deep auto-encoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all meaningful sources of variation and their cardinality.
A 3D Face Model for Pose and Illumination Invariant Face Recognition, 2009
Paysan, P., Knothe, R., Amberg, B., Romdhani, S., and Vetter, T · 2009
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
Nguyen, X., Wainwright, M. J., and Jordan, M. I · 2010
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Density-ratio matching under the Bregman divergence: A unified framework of density-ratio estimation
Sugiyama, M., Suzuki, T., and Kanamori, T · 2012
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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, 2014
Kingma, D. P. and Welling, M · 2014
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Learning to disentangle factors of variation with manifold interaction, 2014
Reed, S., Sohn, K., Zhang, Y., and Lee, H · 2014
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Deep convolutional inverse graphics network, 2015
Kulkarni, T. D., Whitney, W. F., Kohli, P., and Tenenbaum, J · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Weakly-supervised disentangling with recurrent transformations for 3D view synthesis, 2015
Yang, J., Reed, S. E., Yang, M.-H., and Lee, H · 2015
Cited alongside, same era.
InfoGAN: Interpretable representation learning by information maximizing Generative Adversarial Nets, 2016
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
Cited alongside, same era.
Building machines that learn and think like people, 2016
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J · 2016
Cited alongside, same era.
Makhzani, A., Shlens, J., Jaitly, N., and Goodfellow, I · 2016
Cited alongside, same era.
Disentangling factors of variation in deep representations using adversarial training, 2016
Mathieu, M., Zhao, J., Sprechmann, P., Ramesh, A., and LeCun, Y · 2016
Cited alongside, same era.
dSprites: Disentanglement testing Sprites dataset, 2017
Matthey, L., Higgins, I., Hassabis, D., and Lerchner, A · 2017
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Fixing a broken ELBO, 2018
Alemi, A. A., Poole, B., Fischer, I., Dillon, J. V., Saurous, R. A., and Murphy, K · 2018
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Isolating sources of disentanglement in variational autoencoders
Chen, R. T. Q., Li, X., Grosse, R., and Duvenaud, D · 2018
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Learning disentangled joint continuous and discrete representations, 2018
Dupont, E · 2018
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A framework for the quantitative evaluation of disentangled representations, 2018
Eastwood, C. and Williams, C. K. I · 2018
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Disentangling by factorising
Kim, H. and Mnih, A · 2018
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Understanding visual concepts with continuation learning, 2016
Whitney, W. F., Chang, M., Kulkarni, T., and Tenenbaum, J. B · 2016
Cited alongside, same era.
Learning independent features with adversarial nets for non-linear ICA
Brakel, P. and Bengio, Y · 2017
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.
Variation inference of disentangled latent concepts from unlabeled observations
Kumar, A., Sattigeri, P., and Balakrishnan, A · 2018
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