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Generative Adversarial Networks (GANs) are typically trained to synthesize data, from images and more recently tabular data, under the assumption of directly accessible training data.
Divergence measures based on the shannon entropy
J. Lin · 1991
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Parameter-based reduction of gaussian mixture models with a variational-bayes approach
P. Bruneau, M. Gelgon, and F. Picarougne · 2008
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Kullback-Leibler Divergence
J. M. Joyce · 2011
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Generative adversarial nets
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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UCI machine learning repository
D. Dua and C. Graff · 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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Communication-Efficient Learning of Deep Networks from Decentralized Data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
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On wasserstein two-sample testing and related families of nonparametric tests
A. Ramdas, N. G. Trillos, and M. Cuturi · 2017
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Data synthesis based on generative adversarial networks
N. Park, M. Mohammadi, K. Gorde, S. Jajodia, H. Park, and Y. Kim · 2018
Cited alongside, same era.
On accurate evaluation of gans for language generation
S. Semeniuta, A. Severyn, and S. Gelly · 2018
Cited alongside, same era.
Kaggle - anonymized credit card transactions labeled as fraudulent or genuine
M. L. G. ULB · 2018
Cited alongside, same era.
Synthesizing tabular data using generative adversarial networks
L. Xu and K. Veeramachaneni · 2018
Cited alongside, same era.
Tabnet: Attentive interpretable tabular learning
S. O. Arik and T. Pfister · 2019
Cited alongside, same era.
Synthetic learning: Learn from distributed asynchronized discriminator gan without sharing medical image data
Q. Chang, H. Qu, Y. Zhang, M. Sabuncu, C. Chen, T. Zhang, and D. N. Metaxas · 2020
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Fegan: Scaling distributed gans
R. Guerraoui, A. Guirguis, A.-M. Kermarrec, and E. Le Merrer · 2020
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An exploratory analysis on users’ contributions in federated learning
J. Huang, R. Talbi, Z. Zhao, S. Boucchenak, L. Y. Chen, and S. Roos · 2020
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Analyzing and improving the image quality of stylegan
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 2020
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Learn distributed gan with temporary discriminators
H. Qu, Y. Zhang, Q. Chang, Z. Yan, C. Chen, and D. Metaxas · 2020
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Fedgan: Federated generative adversarial networks for distributed data
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Md-gan: Multi-discriminator generative adversarial networks for distributed datasets
C. Hardy, E. Le Merrer, and B. Sericola · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
Cited alongside, same era.
Modeling tabular data using conditional gan
L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni · 2019
Cited alongside, same era.
M. Rasouli, T. Sun, and R. Rajagopal · 2020
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Ctab-gan: Effective table data synthesizing
Z. Zhao, A. Kunar, R. Birke, and L. Y. Chen · 2021
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