Fetching the paper…
Reading the bibliography…
Computing optimal transport maps between high-dimensional and continuous distributions is a challenging problem in optimal transport (OT).
Brenier, Y.: Polar factorization and monotone rearrangement of vector-valued functions. Communications on pure and applied mathematics 44
1991
Earlier work this paper cites.
Cominetti, R., San Martín, J.: Asymptotic analysis of the exponential penalty trajectory in linear programming. Mathematical Programming 67
1994
Earlier work this paper cites.
Ambrosio, L., Gigli, N., Savaré, G.: Gradient flows: in metric spaces and in the space of probability measures. Springer Science & Business Media (2008)
2008
Earlier work this paper cites.
Villani, C.: Optimal transport, old and new. A Series of comprehensive Studies in Mathematics (2008)
2008
Earlier work this paper cites.
Ambrosio, L., Gigli, N.: A user’s guide to optimal transport. In: Modelling and optimisation of flows on networks, pp. 1–155. Springer (2013)
2013
Earlier work this paper cites.
Cuturi, M.: Sinkhorn distances: Lightspeed computation of optimal transport. In: NIPS. pp. 2292–2300 (2013)
2013
Earlier work this paper cites.
Cuturi, M., Doucet, A.: Fast computation of wasserstein barycenters. In: ICML. pp. 685–693 (2014)
2014
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS. pp. 2672–2680 (2014)
2014
Earlier work this paper cites.
Gramfort, A., Peyré, G., Cuturi, M.: Fast optimal transport averaging of neuroimaging data. In: International Conference on Information Processing in Medical Imaging. pp. 261–272. Springer (2015)
2015
Earlier work this paper cites.
Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A., Du, T., Guibas, L.: Convolutional wasserstein distances: Efficient optimal transportation on geometric domains. ACM Transactions on Graphics (TOG) 34
2015
Earlier work this paper cites.
Genevay, A., Cuturi, M., Peyré, G., Bach, F.: Stochastic optimization for large-scale optimal transport. In: NIPS. pp. 3440–3448 (2016)
2016
Earlier work this paper cites.
Nowozin, S., Cseke, B., Tomioka, R.: f-gan: Training generative neural samplers using variational divergence minimization. In: NIPS. pp. 271–279 (2016)
2016
Earlier work this paper cites.
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017)
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
Courty, N., Flamary, R., Tuia, D., Rakotomamonjy, A.: Optimal transport for domain adaptation. IEEE transactions on pattern analysis and machine intelligence 39
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. In: NIPS. pp. 5767–5777 (2017)
2017
Cited alongside, same era.
Johnson, R., Zhang, T.: Composite functional gradient learning of generative adversarial models. In: ICML. pp. 2376–2384 (2018)
2018
Later among the works it cites.
Lin, Z., Khetan, A., Fanti, G., Oh, S.: Pacgan: The power of two samples in generative adversarial networks. In: NIPS. pp. 1505–1514 (2018)
2018
Later among the works it cites.
Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? In: ICML. pp. 3478–3487 (2018)
2018
Later among the works it cites.
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. ICLR (2018)
2018
Later among the works it cites.
Petzka, H., Fischer, A., Lukovnikov, D.: On the regularization of wasserstein GANs. In: ICLR (2018)
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Nagarajan, V., Kolter, J.Z.: Gradient descent gan optimization is locally stable. In: NIPS. pp. 5585–5595 (2017)
2017
Cited alongside, same era.
Santambrogio, F.: { \{ Euclidean, metric, and Wasserstein } \} gradient flows: an overview. Bulletin of Mathematical Sciences 7
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Almahairi, A., Rajeshwar, S., Sordoni, A., Bachman, P., Courville, A.: Augmented cyclegan: Learning many-to-many mappings from unpaired data. In: ICML. pp. 195–204 (2018)
2018
Cited alongside, same era.
Bottou, L., Arjovsky, M., Lopez-Paz, D., Oquab, M.: Geometrical insights for implicit generative modeling. In: Braverman Readings in Machine Learning. Key Ideas from Inception to Current State, pp. 229–268. Springer (2018)
2018
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
Lu, G., Zhou, Z., Song, Y., Ren, K., Yu, Y.: Guiding the one-to-one mapping in cyclegan via optimal transport. AAAI (2019)
2019
Closest in time.
Peyré, G., Cuturi, M., et al.: Computational optimal transport. Foundations and Trends in Machine Learning 11
2019
Closest in time.
Yamaguchi, S., Koyama, M.: Distributional concavity regularization for gans. In: ICLR (2019)
2019
Closest in time.