[26] Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira. On convergence and stability of gans. arXiv:1705.07215, 2017. 2, 5
[27] Lingke Kong, Chenyu Lian, Detian Huang, Yanle Hu, Qichao Zhou, et al. Breaking the dilemma of medical image-to-image translation. NeurIPS, 34:1964-1978, 2021. 1, 2
[28] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 6
[29] Nupur Kumari, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Ensembling off-the-shelf models for gan training. In CVPR, pages 10651-10662, 2022. 1, 2
[30] Tuomas Kynkänniemi, Tero Karras, Miika Aittala, Timo Aila, and Jaakko Lehtinen. The role of imagenet classes in frechet inception distance. In ICLR, 2023. 1, 2
[31] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436-444, 2015. 1
[32] Ziqiang Li, Chaoyue Wang, Heliang Zheng, Jing Zhang, and Bin Li. Fakeclr: Exploring contrastive learning for solving latent discontinuity in data-efficient gans. In ECCV, pages 598-615, 2022. 1, 2, 6, 7, 8, 23
[33] Kanglin Liu, Wenming Tang, Fei Zhou, and Guoping Qiu. Spectral regularization for combating mode collapse in gans. In ICCV, pages 6382-6390, 2019. 2
[34] Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel. Understanding the effective receptive field in deep convolutional neural networks. NeurIPS, 29, 2016. 4
[35] Tengyu Ma. Lecture Notes for Machine Learning Theory (CS229M/STATS214). 4
[36] Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. Which training methods for gans do actually converge? In ICML, pages 3481-3490. PMLR, 2018. 2, 5
[37] Poorya Mianjy and Raman Arora. On dropout and nuclear norm regularization. In ICML, pages 4575-4584. PMLR, 2019. 4
[38] Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. Spectral normalization for generative adversarial networks. In ICLR, 2018. 2, 4
[39] Alfred Müller. Integral probability metrics and their generating classes of functions. Advances in applied probability, 29(2):429-443, 1997. 3
[40] Yao Ni, Piotr Koniusz, Richard Hartley, and Richard Nock. Manifold learning benefits gans. In CVPR, pages 11265-11274, 2022. 1, 2
[41] Yao Ni, Dandan Song, Xi Zhang, Hao Wu, and Lejian Liao. Cagan: Consistent adversarial training enhanced gans. In IJCAI, pages 2588-2594, 2018. 3
[42] Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. f-gan: Training generative neural samplers using variational divergence minimization. NeurIPS, 29, 2016. 2
[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763, 2021. 8
[44] Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or. Encoding in style: a stylegan encoder for image-to-image translation. In CVPR, pages 2287–2296, 2021. 1, 2
[45] Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann. Stabilizing training of generative adversarial networks through regularization. NeurIPS, 30, 2017. 2
[46] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen. Improved techniques for training gans. In NeurIPS, pages 2234-2242. 2016. 7
[47] Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, and Timo Aila. StyleGAN-T: Unlocking the power of GANs for fast large-scale text-to-image synthesis. In ICML, pages 30105-30118, 2023. 1, 2
[48] Fatemeh Shiri, Xin Yu, Piotr Koniusz, and Fatih Porikli. Face destylization. In DICTA, pages 1-8. IEEE, 2017. 1
[49] Fatemeh Shiri, Xin Yu, Fatih Porikli, Richard Hartley, and Piotr Koniusz. Identity-preserving face recovery from portraits. In WACV, 2018. 1
[50] Fatemeh Shiri, Xin Yu, Fatih Porikli, Richard Hartley, and Piotr Koniusz. Identity-preserving face recovery from stylized portraits. IJCV, 127(6-7):863-883, 2019. 1
[51] Zhangzhang Si and Song-Chun Zhu. Learning hybrid image templates (hit) by information projection. TPAMI, 34(7):1354-1367, 2012. 6
[52] Ivan Skorokhodov, Aliaksandr Siarohin, Yinghao Xu, Jian Ren, Hsin-Ying Lee, Peter Wonka, and Sergey Tulyakov. 3D generation on ImageNet. In ICLR, 2023. 1, 2
[53] Ivan Skorokhodov, Sergey Tulyakov, Yiqun Wang, and Peter Wonka. EpiGRAF: Rethinking training of 3d GANs. In NeurIPS, 2022. 1, 2
[54] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. JMLR, 15(1):1929-1958, 2014. 4
[55] Ming Tao, Bing-Kun Bao, Hao Tang, and Changsheng Xu. Galip: Generative adversarial clips for text-to-image synthesis. CVPR, 2023. 1, 2
[56] Hoang Thanh-Tung, Truyen Tran, and Svetha Venkatesh. Improving generalization and stability of generative adversarial networks. In ICLR, 2019. 2, 3, 5