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Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, and flow-based models.
Pattern recognition and machine learning
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Auto-encoding variational Bayes
D. Kingma and M. Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
D. Rezende, S. Mohamed, and D. Wierstra · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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Generative moment matching networks
Y. Li, K. Swersky, and R. Zemel · 2015
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Variational inference with normalizing flows
D. Rezende and S. Mohamed · 2015
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An architecture for deep, hierarchical generative models
P. Bachman · 2016
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Improving variational autoencoders with inverse autoregressive flow
D. Kingma, T. Salimans, R. Józefowicz, X. Chen, I. Sutskever, and M. Welling · 2016
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Autoencoding beyond pixels using a learned similarity metric
A. Larsen, S. Sønderby, H. Larochelle, and O. Winther · 2016
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Adversarial autoencoders
A. Makhzani, J. Shlens, N. Jaitly, and I. Goodfellow · 2016
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Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
T. Salimans and D. Kingma · 2016
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Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Ladder variational autoencoders
C. Sønderby, T. Raiko, L. Maaløe, S. Sønderby, and O. Winther · 2016
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Pixel recurrent neural networks
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Variational lossy autoencoder
X. Chen, D. Kingma, T. Salimans, Y. Duan, P. Dhariwal, J. Schulman, I. Sutskever, and P. Abbeel · 2017
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Modulating early visual processing by language
H. De Vries, F. Strub, J. Mary, H. Larochelle, O. Pietquin, and A. Courville · 2017
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Density estimation using Real NVP
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2017
Cited alongside, same era.
Adversarial feature learning
J. Donahue, P. Krähenbühl, and T. Darrell · 2017
Cited alongside, same era.
GANs trained by a two time-scale update rule converge to a local Nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Cited alongside, same era.
Deep feature consistent variational autoencoder
X. Hou, L. Shen, K. Sun, and G. Qiu · 2017
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
D. Kingma and P. Dhariwal · 2018
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PacGAN: The power of two samples in generative adversarial networks
Z. Lin, A. Khetan, G. Fanti, and S. Oh · 2018
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Deformable shape completion with graph convolutional autoencoders
O. Litany, A. Bronstein, M. Bronstein, and A. Makadia · 2018
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Auxiliary guided autoregressive variational autoencoders
T. Lucas and J. Verbeek · 2018
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Mixed batches and symmetric discriminators for GAN training
T. Lucas, C. Tallec, Y. Ollivier, and J. Verbeek · 2018
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cGANs with projection discriminator
T. Miyato and M. Koyama · 2018
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Fast generation for convolutional autoregressive models
P. Ramachandran, T. Paine, P. Khorrami, M. Babaeizadeh, S. Chang, Y. Zhang, M. Hasegawa-Johnson, R. Campbell, and T. Huang · 2017
Cited alongside, same era.
Variational approaches for auto-encoding generative adversarial networks
M. Rosca, B. Lakshminarayanan, D. Warde-Farley, and S. Mohamed · 2017
Cited alongside, same era.
Bayesian GAN
Y. Saatchi and A. Wilson · 2017
Cited alongside, same era.
PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
T. Salimans, A. Karpathy, X. Chen, and D. Kingma · 2017
Cited alongside, same era.
Amortised MAP inference for image super-resolution
C. Sønderby, J. Caballero, L. Theis, W. Shi, and F. Huszár · 2017
Cited alongside, same era.
On gradient regularizers for MMD GANs
M. Arbel, D. J. Sutherland, M. Binkowski, and A. Gretton · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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Assessing generative models via precision and recall
M. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly · 2018
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Improving gans using optimal transport
T. Salimans, H. Zhang, A. Radford, and D. Metaxas · 2018
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How good is my GAN?
K. Shmelkov, C. Schmid, and K. Alahari · 2018
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It takes (only) two: Adversarial generator-encoder networks
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2018
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Large scale GAN training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
J. Ho, X. Chen, A. Srinivas, Y. Duan, and P. Abbeel · 2019
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Generating high fidelity images with subscale pixel networks and multidimensional upscaling
J. Menick and N. Kalchbrenner · 2019
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Improving generalization and stability of generative adversarial networks
H. Thanh-Tung, T. Tran, and S. Venkatesh · 2019
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Self-attention generative adversarial networks
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena · 2019
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