[31] Atsuhiro Noguchi and Tatsuya Harada. Image generation from small datasets via batch statistics adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019.
[32] Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. f-gan: Training generative neural samplers using variational divergence minimization. In Advances in Neural Information Processing Systems, 2016.
[33] Parth Patel, Nupur Kumari, Mayank Singh, and Balaji Krishnamurthy. Lt-gan: Self-supervised gan with latent transformation detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021.
[34] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems, 2016.
[35] Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger. Projected gans converge faster. In Advances in Neural Information Processing Systems, 2021.
[36] Zhangzhang Si and Song-Chun Zhu. Learning hybrid image templates (hit) by information projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011.
[37] Inder Jeet Taneja. On mean divergence measures. Advances in Inequalities from probability theory and statistics. Nova, USA, 2008.
[38] Ngoc-Trung Tran, Viet-Hung Tran, Bao-Ngoc Nguyen, Linxiao Yang, and Ngai-Man (Man) Cheung. Self-supervised gan: Analysis and improvement with multi-class minimax game. In Advances in Neural Information Processing Systems, 2019.
[39] Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Trung-Kien Nguyen, and Ngai-Man Cheung. On data augmentation for gan training. IEEE Transactions on Image Processing, 2021. doi: 10.1109/TIP.2021.3049346.
[40] Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, and Weilong Yang. Regularizing generative adversarial networks under limited data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[41] Yaxing Wang, Chenshen Wu, Luis Herranz, Joost van de Weijer, Abel Gonzalez-Garcia, and Bogdan Raducanu. Transferring gans: generating images from limited data. In Proceedings of the European Conference on Computer Vision (ECCV), 2018.
[42] Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, and Joost van de Weijer. Minegan: Effective knowledge transfer from gans to target domains with few images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[43] Ceyuan Yang, Yujun Shen, Yinghao Xu, and Bolei Zhou. Data-efficient instance generation from instance discrimination. In Advances in Neural Information Processing Systems, 2021.
[44] mengping yang, Zhe Wang, Ziqiu Chi, and Yanbing Zhang. Fregan: Exploiting frequency components for training gans under limited data. In Advances in Neural Information Processing Systems, 2022.
[45] Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015.
[46] Han Zhang, Zizhao Zhang, Augustus Odena, and Honglak Lee. Consistency regularization for generative adversarial networks. In International Conference on Learning Representations, 2020.
[47] Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han. Differentiable augmentation for data-efficient gan training. In Advances in Neural Information Processing Systems, 2020.
[48] Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, and Han Zhang. Image augmentations for gan training. arXiv preprint arXiv:2006.02595, 2020.