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Learning useful representations with little or no supervision is a key challenge in artificial intelligence.
S. Watanabe, “Information theoretical analysis of multivariate correlation,” IBM Journal of Research and Development , vol. 4, no. 1, pp. 66–82, 1960
1960
Earlier work this paper cites.
A. Makhzani and B. J. Frey, “PixelGAN autoencoders,” in Advances in Neural Information Processing Systems , 2017, pp. 1975–1985
1985
Earlier work this paper cites.
G. E. Hinton and D. Van Camp, “Keeping the neural networks simple by minimizing the description length of the weights,” in Proc. of the Annual Conference on Computational Learning Theory , 1993, pp. 5–13
1993
Earlier work this paper cites.
N. Tishby, F. C. Pereira, and W. Bialek, “The information bottleneck method,” arXiv preprint physics/0004057 , 2000
2000
Earlier work this paper cites.
A. Honkela and H. Valpola, “Variational learning and bits-back coding: An information-theoretic view to bayesian learning,” IEEE Transactions on Neural Networks , vol. 15, no. 4, pp. 800–810, 2004
2004
Earlier work this paper cites.
A. Gretton, O. Bousquet, A. Smola, and B. Schölkopf, “Measuring statistical dependence with Hilbert-Schmidt norms,” in International Conference on Algorithmic Learning Theory . Springer, 2005, pp. 63–77
2005
Earlier work this paper cites.
Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layer-wise training of deep networks,” in Advances In Neural Information Processing Systems , 2007, pp. 153–160
2007
Earlier work this paper cites.
M. A. Ranzato, C. Poultney, S. Chopra, and Y. LeCun, “Efficient learning of sparse representations with an energy-based model,” in Advances in Neural Information Processing Systems , 2007, pp. 1137–1144
2007
Earlier work this paper cites.
A. Rahimi and B. Recht, “Random features for large-scale kernel machines,” in Advances in Neural Information Processing Systems , 2008, pp. 1177–1184
2008
Earlier work this paper cites.
M. J. Wainwright and M. I. Jordan, “Graphical models, exponential families, and variational inference,” Foundations and Trends® in Machine Learning , vol. 1, no. 1–2, pp. 1–305, 2008
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proc. of the International Conference on Machine Learning , 2008, pp. 1096–1103
2008
Earlier work this paper cites.
P. Paysan, R. Knothe, B. Amberg, S. Romdhani, and T. Vetter, “A 3d face model for pose and illumination invariant face recognition,” in Proc. of the IEEE International Conference on Advanced Video and Signal Based Surveillance . Ieee, 2009, pp. 296–301
2009
Earlier work this paper cites.
X. Nguyen, M. J. Wainwright, and M. I. Jordan, “Estimating divergence functionals and the likelihood ratio by convex risk minimization,” IEEE Transactions on Information Theory , vol. 56, no. 11, pp. 5847–5861, 2010
2010
Earlier work this paper cites.
M. Sugiyama, T. Suzuki, and T. Kanamori, “Density-ratio matching under the bregman divergence: a unified framework of density-ratio estimation,” Annals of the Institute of Statistical Mathematics , vol. 64, no. 5, pp. 1009–1044, 2012
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” Journal of Machine Learning Research , vol. 13, no. Mar, 2012
2012
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, “Semi-supervised learning with deep generative models,” in Advances in Neural Information Processing Systems , 2014, pp. 3581–3589
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” in Proc. of the International Conference on Machine Learning , 2014, pp. 1278–1286
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
M. Aubry, D. Maturana, A. A. Efros, B. C. Russell, and J. Sivic, “Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models,” in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 3762–3769
2014
Earlier work this paper cites.
A. Mnih and K. Gregor, “Neural variational inference and learning in belief networks,” in Proc. of the International Conference on Machine Learning , 2014, pp. 1791–1799
2014
Earlier work this paper cites.
T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. Tenenbaum, “Deep convolutional inverse graphics network,” in Advances in Neural Information Processing Systems , 2015, pp. 2539–2547
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proc. of the IEEE International Conference on Computer Vision , 2015, pp. 3730–3738
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. A. Alemi, I. Fischer, J. V. Dillon, and K. Murphy, “Deep variational information bottleneck,” in International Conference on Learning Representations , 2016
2016
Earlier work this paper cites.
C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel, “The variational fair autoencoder,” in International Conference on Learning Representations , 2016
2016
Earlier work this paper cites.
C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, and O. Winther, “Ladder variational autoencoders,” in Advances in Neural Information Processing Systems , 2016, pp. 3738–3746
2016
Earlier work this paper cites.
M. Johnson, D. K. Duvenaud, A. Wiltschko, R. P. Adams, and S. R. Datta, “Composing graphical models with neural networks for structured representations and fast inference,” in Advances in Neural Information Processing Systems , 2016, pp. 2946–2954
2016
Cited alongside, same era.
C. Doersch, “Tutorial on variational autoencoders,” arXiv:1606.05908 , 2016
2016
Cited alongside, same era.
M. D. Hoffman and M. J. Johnson, “Elbo surgery: yet another way to carve up the variational evidence lower bound,” in Workshop in Advances in Approximate Bayesian Inference, NIPS , 2016
2016
Cited alongside, same era.
Q. Liu and D. Wang, “Stein variational gradient descent: A general purpose bayesian inference algorithm,” in Advances In Neural Information Processing Systems , 2016, pp. 2378–2386
2016
Cited alongside, same era.
V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville, “Adversarially learned inference,” in International Conference on Learning Representations , 2017
2017
Later among the works it cites.
J. Donahue, P. Krähenbühl, and T. Darrell, “Adversarial feature learning,” in International Conference on Learning Representations , 2017
2017
Later among the works it cites.
C. Li, H. Liu, C. Chen, Y. Pu, L. Chen, R. Henao, and L. Carin, “Alice: Towards understanding adversarial learning for joint distribution matching,” in Advances in Neural Information Processing Systems , 2017, pp. 5495–5503
2017
Later among the works it cites.
2017
Later among the works it cites.
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A. van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, and A. Graves, “Conditional image generation with PixelCNN decoders,” in Advances in Neural Information Processing Systems , 2016, pp. 4790–4798
2016
Cited alongside, same era.
A. Van Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” in International Conference on Machine Learning , 2016, pp. 1747–1756
2016
Cited alongside, same era.
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling, “Improved variational inference with inverse autoregressive flow,” in Advances in Neural Information Processing Systems , 2016, pp. 4743–4751
2016
Cited alongside, same era.
P. Bachman, “An architecture for deep, hierarchical generative models,” in Advances in Neural Information Processing Systems , 2016, pp. 4826–4834
2016
Cited alongside, same era.
C. J. Maddison, A. Mnih, and Y. W. Teh, “The concrete distribution: A continuous relaxation of discrete random variables,” in International Conference on Learning Representations , 2016
2016
Cited alongside, same era.
A. Mnih and D. J. Rezende, “Variational inference for monte carlo objectives,” in Proc. of the International Conference on Machine Learning , 2016, pp. 2188–2196
2016
Cited alongside, same era.
S. R. Bowman, L. Vilnis, O. Vinyals, A. Dai, R. Jozefowicz, and S. Bengio, “Generating sentences from a continuous space,” in Proc. of the SIGNLL Conference on Computational Natural Language Learning , 2016, pp. 10–21
2016
Cited alongside, same era.
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets,” in Advances in Neural Information Processing Systems , 2016, pp. 2172–2180
2016
Cited alongside, same era.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proc. of the International Conference on Machine Learning , vol. 70, 2017, pp. 214–223
2017
Later among the works it cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5967–5976
2017
Later among the works it cites.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proc. of the IEEE International Conference on Computer Vision , 2017, pp. 2242–2251
2017
Later among the works it cites.
H. Kim and A. Mnih, “Disentangling by factorising,” in Proc. of the International Conference on Machine Learning , 2018, pp. 2649–2658
2018
Closest in time.
T. Q. Chen, X. Li, R. Grosse, and D. Duvenaud, “Isolating sources of disentanglement in variational autoencoders,” in Advances in Neural Information Processing Systems , 2018
2018
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A. Kumar, P. Sattigeri, and A. Balakrishnan, “Variational inference of disentangled latent concepts from unlabeled observations,” in International Conference on Learning Representations , 2018
2018
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R. Lopez, J. Regier, M. I. Jordan, and N. Yosef, “Information constraints on auto-encoding variational bayes,” in Advances in Neural Information Processing Systems , 2018
2018
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2018
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A. Achille and S. Soatto, “Information dropout: Learning optimal representations through noisy computation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2018
2018
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E. Dupont, “Learning disentangled joint continuous and discrete representations,” in Advances in Neural Information Processing Systems , 2018
2018
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H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. Singh, and M.-H. Yang, “Diverse image-to-image translation via disentangled representations,” in Proc. of the European Conference on Computer Vision , 2018, pp. 35–51
2018
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A. Gonzalez-Garcia, J. van de Weijer, and Y. Bengio, “Image-to-image translation for cross-domain disentanglement,” in Advances in Neural Information Processing Systems , 2018
2018
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A. Alemi, B. Poole, I. Fischer, J. Dillon, R. A. Saurous, and K. Murphy, “Fixing a broken ELBO,” in Proc. of the International Conference on Machine Learning , 2018, pp. 159–168
2018
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I. Tolstikhin, O. Bousquet, S. Gelly, and B. Schoelkopf, “Wasserstein auto-encoders,” in International Conference on Learning Representations , 2018
2018
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N. Hadad, L. Wolf, and M. Shahar, “A two-step disentanglement method,” in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 772–780
2018
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C. Eastwood and C. K. I. Williams, “A framework for the quantitative evaluation of disentangled representations,” in International Conference on Learning Representations , 2018
2018
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K. Ridgeway and M. C. Mozer, “Learning deep disentangled embeddings with the f-statistic loss,” in Advances in Neural Information Processing Systems , 2018
2018
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2018
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2018
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A. A. Alemi and I. Fischer, “TherML: Thermodynamics of machine learning,” arXiv:1807.04162 , 2018
2018
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L. Yingzhen and S. Mandt, “Disentangled sequential autoencoder,” in Proc. of the International Conference on Machine Learning , 2018, pp. 5656–5665
2018
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J.-T. Hsieh, B. Liu, D.-A. Huang, L. Fei-Fei, and J. C. Niebles, “Learning to decompose and disentangle representations for video prediction,” in Advances in Neural Information Processing Systems , 2018
2018
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2018
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M. Tschannen, E. Agustsson, and M. Lucic, “Deep generative models for distribution-preserving lossy compression,” in Advances in Neural Information Processing Systems , 2018
2018
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A. Liu, Y.-C. Liu, Y.-Y. Yeh, and Y.-C. F. Wang, “A unified feature disentangler for multi-domain image translation and manipulation,” in Advances in Neural Information Processing Systems , 2018
2018
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