[21] M. Kang, J.-Y. Zhu, R. Zhang, J. Park, E. Shechtman, S. Paris, and T. Park. Scaling up GANs for text-to-image synthesis. In IEEE Conf. Comput. Vis. Pattern Recog., 2023.
[22] T. Karras, T. Aila, S. Laine, and J. Lehtinen. Progressive growing of GANs for improved quality, stability, and variation. In Int. Conf. Learn. Represent., 2018.
[23] T. Karras, S. Laine, and T. Aila. A style-based generator architecture for generative adversarial networks. In IEEE Conf. Comput. Vis. Pattern Recog., 2019.
[24] T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila. Analyzing and improving the image quality of StyleGAN. In IEEE Conf. Comput. Vis. Pattern Recog., 2020.
[25] T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila. Analyzing and improving the image quality of stylegan. In IEEE Conf. Comput. Vis. Pattern Recog., 2020.
[26] T. Karras, M. Aittala, S. Laine, E. Härkönen, J. Hellsten, J. Lehtinen, and T. Aila. Alias-free generative adversarial networks. In Adv. Neural Inform. Process. Syst., 2021.
[27] D. P. Kingma and M. Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
[28] T. Kynkänniemi, T. Karras, M. Aittala, T. Aila, and J. Lehtinen. The role of ImageNet classes in fr\`echet inception distance. In Int. Conf. Learn. Represent., 2023.
[29] G. Lee, H. Kim, J. Kim, S. Kim, J.-W. Ha, and Y. Choi. Generator knows what discriminator should learn in unconditional GANs. In Eur. Conf. Comput. Vis., 2022.
[30] L. Mescheder, A. Geiger, and S. Nowozin. Which training methods for GANs do actually converge? In Int. Conf. Mach. Learn., 2018.
[31] C. Nash, J. Menick, S. Dieleman, and P. W. Battaglia. Generating images with sparse representations. In Int. Conf. Mach. Learn., 2021.
[32] T. Nguyen-Phuoc, C. Li, L. Theis, C. Richardt, and Y.-L. Yang. HoloGAN: Unsupervised learning of 3d representations from natural images. In Int. Conf. Comput. Vis., 2019.
[33] W. Peebles, J. Peebles, J.-Y. Zhu, A. Efros, and A. Torralba. The hessian penalty: A weak prior for unsupervised disentanglement. In Eur. Conf. Comput. Vis., 2020.
[34] A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In Int. Conf. Learn. Represent., 2016.
[35] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In Int. Conf. Mach. Learn., 2021.
[36] M. Saito, E. Matsumoto, and S. Saito. Temporal generative adversarial nets with singular value clipping. In Int. Conf. Comput. Vis., 2017.
[37] M. Saito, S. Saito, M. Koyama, and S. Kobayashi. Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan. Int. J. Comput. Vis., 2020.
[38] A. Sauer, K. Schwarz, and A. Geiger. StyleGAN-XL: Scaling StyleGAN to large diverse datasets. In SIGGRAPH, 2022.
[39] A. Sauer, T. Karras, S. Laine, A. Geiger, and T. Aila. StyleGAN-T: Unlocking the power of GANs for fast large-scale text-to-image synthesis. In Int. Conf. Mach. Learn., 2023.
[40] Z. Shi, Y. Shen, J. Zhu, D.-Y. Yeung, and Q. Chen. 3d-aware indoor scene synthesis with depth priors. In Eur. Conf. Comput. Vis., 2022.
[41] A. Siarohin, S. Lathuilière, S. Tulyakov, E. Ricci, and N. Sebe. First order motion model for image animation. In Adv. Neural Inform. Process. Syst., 2019.
[42] I. Skorokhodov, S. Tulyakov, and M. Elhoseiny. Stylegan-V: A continuous video generator with the price, image quality and perks of StyleGAN2. In IEEE Conf. Comput. Vis. Pattern Recog., 2022.
[43] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In Int. Conf. Mach. Learn., pages 2256-2265. PMLR, 2015.