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The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research.
Multiobjective optimization: Interactive and evolutionary approaches
J. Branke, J. Branke, K. Deb, K. Miettinen, and R. Slowiński · 2008
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Distance from a point to an ellipse, an ellipsoid, or a hyperellipsoid
D. Eberly · 2011
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Generative Adversarial Networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Semi-supervised learning with deep generative models
D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Density estimation using Real NVP
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2016
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Unrolled generative adversarial networks
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein · 2016
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Improved techniques for training GANs
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Pixel recurrent neural networks
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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Conditional image generation with PixelCNN decoders
A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves, and K. Kavukcuoglu · 2016
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Do GANs actually learn the distribution? An empirical study
S. Arora and Y. Zhang · 2017
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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
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Arbitrary style transfer in real-time with adaptive instance normalization
X. Huang and S. J. Belongie · 2017
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Progressive growing of GANs for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2017
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PacGAN: The power of two samples in generative adversarial networks
Z. Lin, A. Khetan, G. Fanti, and S. Oh · 2017
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Revisiting classifier two-sample tests
D. Lopez-Paz and M. Oquab · 2017
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Which training methods for GANs do actually converge?
L. Mescheder, A. Geiger, and S. Nowozin · 2018
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Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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cGANs with projection discriminator
T. Miyato and M. Koyama · 2018
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Assessing generative models via precision and recall
M. S. M. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly · 2018
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Wasserstein auto-encoders
I. Tolstikhin, O. Bousquet, S. Gelly, and B. Schoelkopf · 2018
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Megapixel size image creation using generative adversarial networks
M. Marchesi · 2017
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Conditional image synthesis with auxiliary classifier GANs
A. Odena, C. Olah, and J. Shlens · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
J. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Cited alongside, same era.
M. Bińkowski, D. J. Sutherland, M. Arbel, and A. Gretton · 2018
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Flow-GAN: Combining maximum likelihood and adversarial learning in generative models
A. Grover, M. Dhar, and S. Ermon · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
D. P. Kingma and P. Dhariwal · 2018
Cited alongside, same era.
Self-attention generative adversarial networks
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 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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A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
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Do deep generative models know what they don’t know?
E. Nalisnick, A. Matsukawa, Y. W. Teh, D. Gorur, and B. Lakshminarayanan · 2019
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Revisiting precision and recall definition for generative model evaluation
L. Simon, R. Webster, and J. Rabin · 2019
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