[22] G. Peyre and M. Cuturi, “Computational optimal transport,” Foundations and Trends in Machine Learning, 2019.
[23] M. Arjovsky, S. Chintala, and L. Bottou, "Wasserstein generative adversarial networks," in Proceedings of the 34th International Conference on Machine Learning, 2017.
[24] V. Panaretos and Y. Zemel, An Invitation to Statistics in Wasserstein Space. Creative Media Partners, LLC, 2020.
[25] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, "Gans trained by a two time-scale update rule converge to a local nash equilibrium," in Advances in Neural Information Processing Systems, 2017.
[26] C. Villani, Optimal Transport: Old and New. Grundlehren der mathematischen Wissenschaften, Springer Berlin Heidelberg, 2008.
[27] D. P. Kingma and M. Welling, "Auto-Encoding Variational Bayes," in 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, 2014.
[28] G. Wallace, “The JPEG still picture compression standard,” IEEE Transactions on Consumer Electronics, 1992.
[29] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
[30] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing, 2004.
[31] R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, "The unreasonable effectiveness of deep features as a perceptual metric," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[32] J. Johnson, A. Alahi, and L. Fei-Fei, "Perceptual losses for real-time style transfer and super-resolution," in European Conference on Computer Vision (ECCV), 2016.
[33] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen, "Improved techniques for training gans," in Advances in Neural Information Processing Systems, 2016.
[34] M. Binkowski, D. J. Sutherland, M. Arbel, and A. Gretton, “Demystifying MMD GANs,” in International Conference on Learning Representations, 2018.
[35] C. Saharia, J. Ho, W. Chan, T. Salimans, D. Fleet, and M. Norouzi, "Image super-resolution via iterative refinement," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
[36] E. Agustsson and R. Timofte, "Ntire 2017 challenge on single image super-resolution: Dataset and study," in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017.
[37] P. von Platen, S. Patil, A. Lozhkov, P. Cuenca, N. Lambert, K. Rasul, M. Davaadorj, and T. Wolf, "Diffusers: State-of-the-art diffusion models." https://github.com/huggingface/diffusers, 2022.
[38] A. Alemi, B. Poole, I. Fischer, J. Dillon, R. A. Saurous, and K. Murphy, "Fixing a broken ELBO," in Proceedings of the 35th International Conference on Machine Learning, 2018.
[39] G. Ohayon, T. Adrai, M. Elad, and T. Michaeli, "Reasons for the superiority of stochastic estimators over deterministic ones: Robustness, consistency and perceptual quality," 2022.
[40] J. T. Flam, S. Chatterjee, K. Kansanen, and T. Ekman, "On mmse estimation: A linear model under gaussian mixture statistics," IEEE Transactions on Signal Processing, 2012.
[41] I. Kligvasser, T. Shaham, Y. Bahat, and T. Michaeli, "Deep self-dissimilarities as powerful visual fingerprints," Advances in Neural Information Processing Systems, 2021.
[42] T. Rott Shaham, T. Dekel, and T. Michaeli, "Singan: Learning a generative model from a single natural image," in Computer Vision (ICCV), IEEE International Conference on, 2019.