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Generative Adversial Networks (GANs) have made a major impact in computer vision and machine learning as generative models.
Quantization and the method of k-means
Pollard, D · 1982
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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The geometry of dissipative evolution equations: the porous medium equation
Otto, F · 2001
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Optimal transport: old and new , volume 338
Villani, C · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Finsler structure in the p-Wasserstein space and gradient flows
Agueh, M · 2012
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Learning probability measures with respect to optimal transport metrics
Canas, G. and Rosasco, L · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Sliced and Radon Wasserstein barycenters of measures
Bonneel, N., Rabin, J., Peyré, G., and Pfister, H · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
Cited alongside, same era.
Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Cited alongside, same era.
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
GANs trained by a two time-scale update rule converge to a local Nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
Mallasto, A. and Feragen, A · 2017
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Computational optimal transport
Peyré, G. and Cuturi, M · 2017
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Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B · 2017
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Generative modeling using the sliced Wasserstein distance
Deshpande, I., Zhang, Z., and Schwing, A · 2018
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Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., and Müller, K.-R · 2017
Cited alongside, same era.
Optimal transport for domain adaptation
Courty, N., Flamary, R., Tuia, D., and Rakotomamonjy, A · 2017
Cited alongside, same era.
Learning generative models with Sinkhorn divergences
Genevay, A., Peyré, G., and Cuturi, M · 2017
Cited alongside, same era.
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
Cited alongside, same era.
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Generalizing point embeddings using the Wasserstein space of elliptical distributions
Muzellec, B. and Cuturi, M · 2018
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Wasserstein divergence for gans
Wu, J., Huang, Z., Thoma, J., Acharya, D., and Van Gool, L · 2018
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