[48] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10684-10695, 2022.
[49] M. S. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly. Assessing generative models via precision and recall. Adv. Neural Inform. Process. Syst., 31, 2018.
[50] A. Sauer, K. Chitta, J. Müller, and A. Geiger. Projected gans converge faster. Adv. Neural Inform. Process. Syst., pages 17480-17492, 2021.
[51] A. Sauer, K. Schwarz, and A. Geiger. Stylegan-xl: Scaling stylegan to large diverse datasets. In ACM SIGGRAPH, pages 1-10, 2022.
[52] 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. arXiv preprint arXiv:2301.09515, 2023.
[53] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. Int. Conf. Learn. Represent., 2015.
[54] J. Song, C. Meng, and S. Ermon. Denoising diffusion implicit models. In Int. Conf. Learn. Represent., 2021.
[55] Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole. Score-based generative modeling through stochastic differential equations. In Int. Conf. Learn. Represent., 2021.
[56] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the inception architecture for computer vision. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2818-2826, 2016.
[57] H. Tang, H. Liu, D. Xu, P. H. Torr, and N. Sebe. AttentionGAN: Unpaired image-to-image translation using attention-guided generative adversarial networks. IEEE Trans. Neural Networks Learn. Syst., 2021.
[58] T. A. Tero Karras, Samuli Laine. Flickr-faces-hq dataset (ffhq). URL https://github.com/NVlabs/ ffhq-dataset.
[59] I. O. Tolstikhin, N. Houlsby, A. Kolesnikov, L. Beyer, X. Zhai, T. Unterthiner, J. Yung, A. Steiner, D. Keysers, J. Uszkoreit, et al. Mlp-mixer: An all-mlp architecture for vision. Adv. Neural Inform. Process. Syst., 34:24261–24272, 2021.
[60] H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jegou. Training data-efficient image transformers & distillation through attention. In Int. Conf. Mach. Learn., pages 10347-10357, 2021.
[61] H. Touvron, P. Bojanowski, M. Caron, M. Cord, A. El-Nouby, E. Grave, G. Izacard, A. Joulin, G. Synnaeve, J. Verbeek, et al. Resmlp: Feedforward networks for image classification with data-efficient training. IEEE Trans. Pattern Anal. Mach. Intell., 2022.
[62] J. Wang, C. Yang, Y. Xu, Y. Shen, H. Li, and B. Zhou. Improving gan equilibrium by raising spatial awareness. In IEEE Conf. Comput. Vis. Pattern Recog., pages 11285-11293, 2022.
[63] P. Wang, Y. Li, and N. Vasconcelos. Rethinking and improving the robustness of image style transfer. In IEEE Conf. Comput. Vis. Pattern Recog., pages 124-133, 2021.
[64] Z. Wang, H. Zheng, P. He, W. Chen, and M. Zhou. Diffusion-gan: Training gans with diffusion. arXiv preprint arXiv:2206.02262, 2022.
[65] Z. Xia, X. Pan, S. Song, L. E. Li, and G. Huang. Vision transformer with deformable attention. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4794-4803, 2022.
[66] C. Yang, Y. Shen, Y. Xu, and B. Zhou. Data-efficient instance generation from instance discrimination. Adv. Neural Inform. Process. Syst., pages 9378-9390, 2021.
[67] C. Yang, Y. Shen, Y. Xu, D. Zhao, B. Dai, and B. Zhou. Improving gans with a dynamic discriminator. In Adv. Neural Inform. Process. Syst., 2022.
[68] S. Yang, L. Jiang, Z. Liu, and C. C. Loy. Unsupervised image-to-image translation with generative prior. In IEEE Conf. Comput. Vis. Pattern Recog., pages 18332-18341, 2022.
[69] F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015.
[70] L. Zhang and M. Agrawala. Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543, 2023.
[71] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Int. Conf. Comput. Vis., pages 2223-2232, 2017.