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With the increasingly widespread deployment of generative models, there is a mounting need for a deeper understanding of their behaviors and limitations.
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M. D. Hoffman and M. J. Johnson · 2016
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Disentangling factors of variation in deep representation using adversarial training
M. F. Mathieu, J. J. Zhao, J. Zhao, A. Ramesh, P. Sprechmann, and Y. LeCun · 2016
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Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
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S. Mohamed and B. Lakshminarayanan · 2016
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Plug & play generative networks: Conditional iterative generation of images in latent space
A. Nguyen, J. Yosinski, Y. Bengio, A. Dosovitskiy, and J. Clune · 2016
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Conditional image synthesis with auxiliary classifier GANs
A. Odena, C. Olah, and J. Shlens · 2016
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One-shot generalization in deep generative models
D. Rezende, I. Danihelka, K. Gregor, D. Wierstra, et al · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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A note on the evaluation of generative models
L. Theis, A. van den Oord, and M. Bethge · 2016
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An information-theoretic analysis of deep latent-variable models
A. A. Alemi, B. Poole, I. Fischer, J. V. Dillon, R. A. Saurous, and K. Murphy · 2017
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L. Mescheder, S. Nowozin, and A. Geiger · 2017
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Unrolled generative adversarial networks
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein · 2017
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Masked autoregressive flow for density estimation
G. Papamakarios, I. Murray, and T. Pavlakou · 2017
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Adversarial symmetric variational autoencoder
Y. Pu, W. Wang, R. Henao, L. Chen, Z. Gan, C. Li, and L. Carin · 2017
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Variational approaches for auto-encoding generative adversarial networks
M. Rosca, B. Lakshminarayanan, D. Warde-Farley, and S. Mohamed · 2017
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Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2017
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VEEGAN: Reducing mode collapse in GANs using implicit variational learning
A. Srivastava, L. Valkov, C. Russell, M. Gutmann, and C. Sutton · 2017
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Many paths to equilibrium: GANs do not need to decrease a divergence at every step
W. Fedus, M. Rosca, B. Lakshminarayanan, A. M. Dai, S. Mohamed, and I. Goodfellow · 2018
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Quantitatively evaluating GANs with divergences proposed for training
D. Jiwoong Im, A. He Ma, G. W. Taylor, and K. Branson · 2018
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Wasserstein auto-encoders
I. Tolstikhin, O. Bousquet, S. Gelly, and B. Schoelkopf · 2018
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