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One of the biggest challenges in the research of generative adversarial networks (GANs) is assessing the quality of generated samples and detecting various levels of mode collapse.
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Algebraic topology
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Topological estimation using witness complexes
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Zomorodian, A. and Carlsson, G · 2005
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Computational homology , volume 157
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Ghrist, R · 2008
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Manifold reconstruction in arbitrary dimensions using witness complexes
Boissonnat, J.-D., Guibas, L. J., and Oudot, S. Y · 2009
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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
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Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X., and Chen, X · 2016
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
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GANs trained by a two time-scale update rule converge to a Nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Klambauer, G., and Hochreiter, S · 2017
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Are gans created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O · 2017
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Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y · 2016
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Generative adversarial imitation learning
Ho, J. and Ermon, S · 2016
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Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Smolley, S. P · 2016
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Paganini, M., de Oliveira, L., and Nachman, B · 2017
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The Riemannian geometry of deep generative models
Shao, H., Kumar, A., and Fletcher, P. T · 2017
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Barratt, S. and Sharma, R · 2018
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