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Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning.
Optimal transport: old and new
C. Villani · 2008
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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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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Sliced and radon wasserstein barycenters of measures
N. Bonneel, J. Rabin, G. Peyré, and H. Pfister · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
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The radon cumulative distribution transform and its application to image classification
S. Kolouri, S. R. Park, and G. K. Rohde · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
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Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou · 2017
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Wasserstein gan
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
S. Arora, R. Ge, Y. Liang, T. Ma, and Y. Zhang · 2017
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Began: Boundary equilibrium generative adversarial networks
D. Berthelot, T. Schumm, and L. Metz · 2017
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Magan: Margin adaptation for generative adversarial networks
R. W. A. Cully, H. J. Chang, and Y. Demiris · 2017
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Adversarial feature learning
J. Donahue, P. Krähenbühl, and T. Darrell · 2017
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Understanding gans: the lqg setting
S. Feizi, C. Suh, F. Xia, and D. Tse · 2017
Cited alongside, same era.
Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 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.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2017
Cited alongside, same era.
Mmd gan: Towards deeper understanding of moment matching network
Dualgan: Unsupervised dual learning for image-to-image translation
Z. Yi, H. Zhang, P. Tan, and M. Gong · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Toward multimodal image-to-image translation
J. Y. Zhu, R. Zhang, D. Pathak, T. Darrell, A. A. Efros, O. Wang, and E. Shechtman · 2017
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Word translation without parallel data
A. Conneau, G. Lample, M. Ranzato, L. Denoyer, and H. Jegou · 2018
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Generative modeling using the sliced wasserstein distance
I. Deshpande, Z. Zhang, and A. Schwing · 2018
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Multimodal Unsupervised Image-to-Image Translation
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C.-L. Li, W.-C. Chang, Y. Cheng, Y. Yang, and B. Póczos · 2017
Cited alongside, same era.
Unsupervised Image-to-Image Translation Networks
M.-Y. Liu, T. Breuel, and J. Kautz · 2017
Cited alongside, same era.
Fisher gan
Y. Mroueh and T. Sercu · 2017
Cited alongside, same era.
Mcgan: Mean and covariance feature matching gan
Y. Mroueh, T. Sercu, and V. Goel · 2017
Cited alongside, same era.
Xgan: Unsupervised image-to-image translation for many-to-many mappings
A. Royer, K. Bousmalis, S. Gouws, F. Bertsch, I. Moressi, F. Cole, and K. Murphy · 2017
Cited alongside, same era.
Learning from simulated and unsupervised images through adversarial training
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb · 2017
Cited alongside, same era.
J. Weed and F. Bach · 2017
Cited alongside, same era.
X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz · 2018
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Sliced-wasserstein autoencoder: An embarrassingly simple generative model
S. Kolouri, C. E. Martin, and G. K. Rohde · 2018
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Sliced wasserstein distance for learning gaussian mixture models
S. Kolouri, G. K. Rohde, and H. Hoffman · 2018
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Diverse image-to-image translation via disentangled representation
H. Y. Lee, H. Y. Tseng, J. B. Huang, M. K. Singh, and M. H. Yang · 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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Which training methods for gans do actually converge?
L. Mescheder, A. Geiger, and S. Nowozin · 2018
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Improving gans using optimal transport
T. Salimans, H. Zhang, A. Radford, and D. Metaxas · 2018
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