[18] Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70, ICML'17, page 214-223. JMLR.org, 2017.
[19] L. N. Vaserstein. Markov processes over denumerable products of spaces, describing large systems of automata. Problemy Peredachi Informatii, 5:64-72, 1969.
[20] Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet. Are gans created equal? a large-scale study. In Advances in Neural Information Processing Systems, volume 31, 2018.
[21] Min Jin Chong and David Forsyth. Effectively unbiased fid and inception score and where to find them. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6069-6078, 2020.
[22] Jianwei Yang, Anitha Kannan, Dhruv Batra, and Devi Parikh. LR-GAN: Layered recursive generative adversarial networks for image generation. In International Conference on Learning Representations, 2017.
[23] Ahmed Alaa, Boris Van Breugel, Evgeny S. Saveliev, and Mihaela van der Schaar. How faithful is your synthetic data? Sample-level metrics for evaluating and auditing generative models. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 290-306. PMLR, 17-23 Jul 2022.
[24] Martin Müller-Lennert, Frédéric Dupuis, Oleg Szehr, Serge Fehr, and Marco Tomamichel. On quantum rényi entropies: A new generalization and some properties. Journal of Mathematical Physics, 54(12):122203, 2013.
[25] Yuxin Dong, Tieliang Gong, Shujian Yu, and Chen Li. Optimal randomized approximations for matrix-based rényi's entropy. IEEE Transactions on Information Theory, 2023.
[26] Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. Improved training of wasserstein gans. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, page 5769-5779, Red Hook, NY, USA, 2017. Curran Associates Inc.
[27] Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. Technical report, University of Toronto, Toronto, Ontario, 2009.
[28] Ya Le and Xuan Yang. Tiny imagenet visual recognition challenge, CS 231N, 2015.
[29] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision – ECCV 2014, pages 740–755. Springer International Publishing, 2014.
[30] Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha. Stargan v2: Diverse image synthesis for multiple domains. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
[31] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4396-4405, 2019.
[32] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPRO9, 2009.
[33] Arash Vahdat and Jan Kautz. NVAE: A deep hierarchical variational autoencoder. In Neural Information Processing Systems (NeurIPS), 2020.
[34] Rewon Child. Very deep  $\{\mathrm{vae}\}$ s generalize autoregressive models and can outperform them on images. In International Conference on Learning Representations, 2021.
[35] Xudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, and Stephen Paul Smolley. Least squares generative adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Oct 2017.