[1] Guillaume Alain and Yoshua Bengio. What regularized auto-encoders learn from the data-generating distribution. JMLR, 15(1):3563–3593, 2014. 5
[2] Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial networks. In ICML, pages 214-223, 2017. 2, 3
[3] Raman Arora, Peter Bartlett, Poorya Mianjy, and Nathan Srebro. Dropout: Explicit forms and capacity control. In ICML, pages 351-361. PMLR, 2021. 4
[4] Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang. Generalization and equilibrium in generative adversarial nets (gans). In ICML, pages 224-232. PMLR, 2017. 2, 3, 5
[5] Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky. Spectrally-normalized margin bounds for neural networks. NeurIPS, 30, 2017. 4
[6] Peter L Bartlett and Shahar Mendelson. Rademacher and gaussian complexities: Risk bounds and structural results. JMLR, 3(Nov):463-482, 2002. 2, 4
[7] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale GAN training for high fidelity natural image synthesis. In ICLR, 2019. 1, 6
[8] Kaiwen Cui, Jiaxing Huang, Zhipeng Luo, Gongjie Zhang, Fangneng Zhan, and Shijian Lu. Genco: generative co-training for generative adversarial networks with limited data. In AAAI, volume 36, pages 499-507, 2022. 3, 6, 8
[9] Kaiwen Cui, Yingchen Yu, Fangneng Zhan, Shengcai Liao, Shijian Lu, and Eric Xing. Kd-dlgan: Data limited image generation via knowledge distillation. In CVPR, 2023. 1, 2, 6, 7, 8
[10] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248-255, 2009. 6
[11] Tiantian Fang, Ruoyu Sun, and Alex Schwing. DigGAN: Discriminator gradient gap regularization for GAN training with limited data. In NeurIPS, 2022. 1, 2, 3, 6, 7, 8
[12] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In ICLR, 2021. 5
[13] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NeurIPS, 2014. 1, 2
[14] Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. Improved training of wasserstein gans. In NeurIPS, pages 5769-5779, 2017. 2, 4
[15] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS, volume 30, pages 6626-6637, 2017. 7
[16] Liang Hou, Huawei Shen, Qi Cao, and Xueqi Cheng. Self-supervised gans with label augmentation. NeurIPS, 34:13019-13031, 2021. 8
[17] Jiaxing Huang, Kaiwen Cui, Dayan Guan, Aoran Xiao, Fangneng Zhan, Shijian Lu, Shengcai Liao, and Eric Xing. Masked generative adversarial networks are data-efficient generation learners. NeurIPS, 35:2154-2167, 2022. 1, 2, 6, 7, 8
[18] Kaiyi Ji, Yi Zhou, and Yingbin Liang. Understanding estimation and generalization error of generative adversarial networks. IEEE Transactions on Information Theory, 67(5):3114-3129, 2021. 2, 3
[19] Liming Jiang, Bo Dai, Wayne Wu, and Chen Change Loy. Deceive d: adaptive pseudo augmentation for gan training with limited data. NeurIPS, 34:21655-21667, 2021. 2
[20] Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park. Scaling up gans for text-to-image synthesis. CVPR, 2023. 1, 2
[21] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of GANs for improved quality, stability, and variation. In ICLR, 2018. 2
[22] Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Training generative adversarial networks with limited data. NeurIPS, 33, 2020. 1, 2, 3, 6, 8, 20, 25, 28, 29
[23] Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free generative adversarial networks. NeurIPS, 34:852-863, 2021. 2
[24] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, 2019. 2, 6
[25] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In CVPR, pages 8110-8119, 2020. 1, 2, 6, 8, 29