{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:20:58Z","timestamp":1780392058969,"version":"3.54.1"},"reference-count":174,"publisher":"Association for Computing Machinery (ACM)","issue":"11","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U19B2044, 61836011, and 91746209"],"award-info":[{"award-number":["U19B2044, 61836011, and 91746209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>\n            Generative Adversarial Networks (GANs) have been widely applied in different scenarios thanks to the development of deep neural networks. The original GAN was proposed based on the non-parametric assumption of the infinite capacity of networks. However, it is still unknown whether GANs can fit the target distribution without any prior information. Due to the overconfident assumption, many issues remain unaddressed in GANs training, such as non-convergence, mode collapses, and gradient vanishing. Regularization and normalization are common methods of introducing prior information to stabilize training and improve discrimination. Although a handful number of regularization and normalization methods have been proposed for GANs, to the best of our knowledge, there exists no comprehensive survey that primarily focuses on objectives and development of these methods, apart from some incomprehensive and limited-scope studies. In this work, we conduct a comprehensive survey on the regularization and normalization techniques from different perspectives of GANs training. First, we systematically describe different perspectives of GANs training and thus obtain the different objectives of regularization and normalization. Based on these objectives, we propose a new taxonomy. Furthermore, we compare the performance of the mainstream methods on different datasets and investigate the applications of regularization and normalization techniques that have been frequently employed in state-of-the-art GANs. Finally, we highlight potential future directions of research in this domain. Code and studies related to the regularization and normalization of GANs in this work are summarized at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/iceli1007\/GANs-Regularization-Review\">https:\/\/github.com\/iceli1007\/GANs-Regularization-Review<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3569928","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T13:09:22Z","timestamp":1667394562000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["A Systematic Survey of Regularization and Normalization in GANs"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9484-2310","authenticated-orcid":false,"given":"Ziqiang","family":"Li","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Heifei, Auhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1811-5207","authenticated-orcid":false,"given":"Muhammad","family":"Usman","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Heifei, Auhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9023-0066","authenticated-orcid":false,"given":"Rentuo","family":"Tao","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Heifei, Auhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8268-2057","authenticated-orcid":false,"given":"Pengfei","family":"Xia","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Heifei, Auhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9002-1029","authenticated-orcid":false,"given":"Chaoyue","family":"Wang","sequence":"additional","affiliation":[{"name":"JD Explore Academy, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3918-384X","authenticated-orcid":false,"given":"Huanhuan","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Heifei, Auhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2332-3959","authenticated-orcid":false,"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Heifei, Auhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"6754","volume-title":"Advances in Neural Information Processing Systems","author":"Adler Jonas","year":"2018","unstructured":"Jonas Adler and Sebastian Lunz. 2018. Banach wasserstein GAN. In Advances in Neural Information Processing Systems. 6754\u20136763."},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Ivan Anokhin Kirill Demochkin Taras Khakhulin Gleb Sterkin Victor Lempitsky and Denis Korzhenkov. 2020. Image Generators with Conditionally-Independent Pixel Synthesis. (2020). arxiv:cs.CV\/2011.13775","DOI":"10.1109\/CVPR46437.2021.01405"},{"key":"e_1_3_2_4_2","article-title":"Towards principled methods for training generative adversarial networks","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky and L\u00e9on Bottou. 2017. Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862 (2017).","journal-title":"arXiv preprint arXiv:1701.04862"},{"key":"e_1_3_2_5_2","unstructured":"Martin Arjovsky and L\u00e9on Bottou. 2017. Towards Principled Methods for Training Generative Adversarial Networks. 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DeshuffleGAN: A self-supervised GAN to improve structure learning. arXiv preprint arXiv:2006.08694 (2020).","journal-title":"arXiv preprint arXiv:2006.08694"},{"key":"e_1_3_2_9_2","first-page":"3821","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Bhaskara Vineeth S.","year":"2022","unstructured":"Vineeth S. Bhaskara, Tristan Aumentado-Armstrong, Allan D. Jepson, and Alex Levinshtein. 2022. GraN-GAN: Piecewise gradient normalization for generative adversarial networks. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 3821\u20133830."},{"key":"e_1_3_2_10_2","first-page":"177","volume-title":"Proceedings of the International Conference on Computational Statistics","author":"Bottou L\u00e9on","year":"2010","unstructured":"L\u00e9on Bottou. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of the International Conference on Computational Statistics. Springer, 177\u2013186."},{"key":"e_1_3_2_11_2","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"Brock Andrew","year":"2018","unstructured":"Andrew Brock, Jeff Donahue, and Karen Simonyan. 2018. Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018).","journal-title":"arXiv preprint arXiv:1809.11096"},{"key":"e_1_3_2_12_2","article-title":"Neural photo editing with introspective adversarial networks","author":"Brock Andrew","year":"2016","unstructured":"Andrew Brock, Theodore Lim, James M. Ritchie, and Nick Weston. 2016. 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In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 7890\u20137899."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.4310\/CDM.1997.v1997.n1.a2"},{"key":"e_1_3_2_33_2","article-title":"Many paths to equilibrium: GANs do not need to decrease a divergence at every step","author":"Fedus William","year":"2017","unstructured":"William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, and Ian Goodfellow. 2017. Many paths to equilibrium: GANs do not need to decrease a divergence at every step. arXiv preprint arXiv:1710.08446 (2017).","journal-title":"arXiv preprint arXiv:1710.08446"},{"key":"e_1_3_2_34_2","article-title":"Consistency-based semi-supervised active learning: Towards minimizing labeling cost","author":"Gao Mingfei","year":"2019","unstructured":"Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan O. Arik, Larry S. Davis, and Tomas Pfister. 2019. 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A variational inequality perspective on generative adversarial networks. arXiv preprint arXiv:1802.10551 (2018).","journal-title":"arXiv preprint arXiv:1802.10551"},{"key":"e_1_3_2_38_2","first-page":"3224","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Gong Xinyu","year":"2019","unstructured":"Xinyu Gong, Shiyu Chang, Yifan Jiang, and Zhangyang Wang. 2019. AutoGAN: Neural architecture search for generative adversarial networks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 3224\u20133234."},{"key":"e_1_3_2_39_2","first-page":"1287","volume-title":"Advances in Neural Information Processing Systems","author":"Gonzalez-Garcia Abel","year":"2018","unstructured":"Abel Gonzalez-Garcia, Joost Van De Weijer, and Yoshua Bengio. 2018. Image-to-image translation for cross-domain disentanglement. In Advances in Neural Information Processing Systems. 1287\u20131298."},{"key":"e_1_3_2_40_2","first-page":"2672","volume-title":"Advances in Neural Information Processing Systems","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. 2672\u20132680."},{"key":"e_1_3_2_41_2","first-page":"5767","volume-title":"Advances in Neural Information Processing Systems","author":"Gulrajani Ishaan","year":"2017","unstructured":"Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C. Courville. 2017. Improved training of Wasserstein GANs. In Advances in Neural Information Processing Systems. 5767\u20135777."},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_43_2","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","author":"Heusel Martin","year":"2017","unstructured":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. arXiv preprint arXiv:1706.08500 (2017).","journal-title":"arXiv preprint arXiv:1706.08500"},{"key":"e_1_3_2_44_2","article-title":"Learning deep representations by mutual information estimation and maximization","author":"Hjelm R. Devon","year":"2018","unstructured":"R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2018. 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Neural Networks 136 (2021), 218\u2013232.","journal-title":"Neural Networks"},{"key":"e_1_3_2_49_2","first-page":"3194","volume-title":"Proceedings of the IEEE Winter Conference on Applications of Computer Vision","author":"Huang Rui","year":"2020","unstructured":"Rui Huang, Wenju Xu, Teng-Yok Lee, Anoop Cherian, Ye Wang, and Tim Marks. 2020. FX-GAN: Self-supervised GAN learning via feature exchange. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision. 3194\u20133202."},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"e_1_3_2_51_2","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015).","journal-title":"arXiv preprint arXiv:1502.03167"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2018.5623"},{"key":"e_1_3_2_53_2","unstructured":"Jongheon Jeong and Jinwoo Shin. 2021. Training GANs with Stronger Augmentations via Contrastive Discriminator. (2021). arxiv:cs.LG\/2103.09742"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3309541"},{"key":"e_1_3_2_55_2","article-title":"Deceive D: Adaptive pseudo augmentation for GAN training with limited data","volume":"34","author":"Jiang Liming","year":"2021","unstructured":"Liming Jiang, Bo Dai, Wayne Wu, and Chen Change Loy. 2021. Deceive D: Adaptive pseudo augmentation for GAN training with limited data. Advances in Neural Information Processing Systems 34 (2021), 21655\u201321667.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_56_2","article-title":"The relativistic discriminator: A key element missing from standard GAN","author":"Jolicoeur-Martineau Alexia","year":"2018","unstructured":"Alexia Jolicoeur-Martineau. 2018. The relativistic discriminator: A key element missing from standard GAN. arXiv preprint arXiv:1807.00734 (2018).","journal-title":"arXiv preprint arXiv:1807.00734"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00782"},{"key":"e_1_3_2_58_2","article-title":"Progressive growing of GANs for improved quality, stability, and variation","author":"Karras Tero","year":"2017","unstructured":"Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017. Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017).","journal-title":"arXiv preprint arXiv:1710.10196"},{"key":"e_1_3_2_59_2","article-title":"Training generative adversarial networks with limited data","volume":"33","author":"Karras Tero","year":"2020","unstructured":"Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. 2020. Training generative adversarial networks with limited data. Advances in Neural Information Processing Systems 33 (2020), 12104\u201312114.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_2_61_2","article-title":"On convergence and stability of GANs","author":"Kodali Naveen","year":"2017","unstructured":"Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira. 2017. On convergence and stability of GANs. arXiv preprint arXiv:1705.07215 (2017).","journal-title":"arXiv preprint arXiv:1705.07215"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00202"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_3_2_64_2","first-page":"950","volume-title":"Advances in Neural Information Processing Systems","author":"Krogh Anders","year":"1992","unstructured":"Anders Krogh and John A. Hertz. 1992. A simple weight decay can improve generalization. In Advances in Neural Information Processing Systems. 950\u2013957."},{"key":"e_1_3_2_65_2","article-title":"Regularization for deep learning: A taxonomy","author":"Kuka\u010dka Jan","year":"2017","unstructured":"Jan Kuka\u010dka, Vladimir Golkov, and Daniel Cremers. 2017. Regularization for deep learning: A taxonomy. arXiv preprint arXiv:1710.10686 (2017).","journal-title":"arXiv preprint arXiv:1710.10686"},{"key":"e_1_3_2_66_2","unstructured":"Karol Kurach Mario Lucic Xiaohua Zhai Marcin Michalski and Sylvain Gelly. 2018. The GAN landscape: Losses architectures regularization and normalization. (2018)."},{"key":"e_1_3_2_67_2","article-title":"A large-scale study on regularization and normalization in GANs","author":"Kurach Karol","year":"2018","unstructured":"Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly. 2018. A large-scale study on regularization and normalization in GANs. arXiv preprint arXiv:1807.04720 (2018).","journal-title":"arXiv preprint arXiv:1807.04720"},{"key":"e_1_3_2_68_2","doi-asserted-by":"crossref","first-page":"164","DOI":"10.3389\/fpubh.2020.00164","article-title":"Generative adversarial networks and its applications in biomedical informatics","volume":"8","author":"Lan Lan","year":"2020","unstructured":"Lan Lan, Lei You, Zeyang Zhang, Zhiwei Fan, Weiling Zhao, Nianyin Zeng, Yidong Chen, and Xiaobo Zhou. 2020. Generative adversarial networks and its applications in biomedical informatics. Frontiers in Public Health 8 (2020), 164.","journal-title":"Frontiers in Public Health"},{"key":"e_1_3_2_69_2","article-title":"Rethinking data augmentation: Self-supervision and self-distillation","author":"Lee Hankook","year":"2019","unstructured":"Hankook Lee, Sung Ju Hwang, and Jinwoo Shin. 2019. Rethinking data augmentation: Self-supervision and self-distillation. arXiv preprint arXiv:1910.05872 (2019).","journal-title":"arXiv preprint arXiv:1910.05872"},{"key":"e_1_3_2_70_2","article-title":"Drit++: Diverse image-to-image translation via disentangled representations","author":"Lee Hsin-Ying","year":"2020","unstructured":"Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao, Jia-Bin Huang, Yu-Ding Lu, Maneesh Singh, and Ming-Hsuan Yang. 2020. Drit++: Diverse image-to-image translation via disentangled representations. International Journal of Computer Vision 128, 10 (2020), 2402\u20132417.","journal-title":"International Journal of Computer Vision"},{"key":"e_1_3_2_71_2","article-title":"InfoMax-GAN: Improved adversarial image generation via information maximization and contrastive learning","author":"Lee Kwot Sin","year":"2020","unstructured":"Kwot Sin Lee, Ngoc-Trung Tran, and Ngai-Man Cheung. 2020. InfoMax-GAN: Improved adversarial image generation via information maximization and contrastive learning. arXiv preprint arXiv:2007.04589 (2020).","journal-title":"arXiv preprint arXiv:2007.04589"},{"key":"e_1_3_2_72_2","article-title":"Regularization methods for generative adversarial networks: An overview of recent studies","author":"Lee Minhyeok","year":"2020","unstructured":"Minhyeok Lee and Junhee Seok. 2020. Regularization methods for generative adversarial networks: An overview of recent studies. arXiv preprint arXiv:2005.09165 (2020).","journal-title":"arXiv preprint arXiv:2005.09165"},{"key":"e_1_3_2_73_2","article-title":"On the limitations of first-order approximation in GAN dynamics","author":"Li Jerry","year":"2017","unstructured":"Jerry Li, Aleksander Madry, John Peebles, and Ludwig Schmidt. 2017. On the limitations of first-order approximation in GAN dynamics. arXiv preprint arXiv:1706.09884 (2017).","journal-title":"arXiv preprint arXiv:1706.09884"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412045"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3071642"},{"key":"e_1_3_2_76_2","article-title":"FakeCLR: Exploring contrastive learning for solving latent discontinuity in data-efficient GANs","author":"Li Ziqiang","year":"2022","unstructured":"Ziqiang Li, Chaoyue Wang, Heliang Zheng, Jing Zhang, and Bin Li. 2022. FakeCLR: Exploring contrastive learning for solving latent discontinuity in data-efficient GANs. arXiv preprint arXiv:2207.08630 (2022).","journal-title":"arXiv preprint arXiv:2207.08630"},{"key":"e_1_3_2_77_2","article-title":"A comprehensive survey on data-efficient GANs in image generation","author":"Li Ziqiang","year":"2022","unstructured":"Ziqiang Li, Xintian Wu, Beihao Xia, Jing Zhang, Chaoyue Wang, and Bin Li. 2022. A comprehensive survey on data-efficient GANs in image generation. arXiv preprint arXiv:2204.08329 (2022).","journal-title":"arXiv preprint arXiv:2204.08329"},{"key":"e_1_3_2_78_2","article-title":"Are high-frequency components beneficial for training of generative adversarial networks","author":"Li Ziqiang","year":"2021","unstructured":"Ziqiang Li, Pengfei Xia, Xue Rui, Yanghui Hu, and Bin Li. 2021. Are high-frequency components beneficial for training of generative adversarial networks. arXiv preprint arXiv:2103.11093 (2021).","journal-title":"arXiv preprint arXiv:2103.11093"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2022.3193373"},{"key":"e_1_3_2_80_2","article-title":"Geometric GAN","author":"Lim Jae Hyun","year":"2017","unstructured":"Jae Hyun Lim and Jong Chul Ye. 2017. Geometric GAN. arXiv preprint arXiv:1705.02894 (2017).","journal-title":"arXiv preprint arXiv:1705.02894"},{"key":"e_1_3_2_81_2","volume-title":"International Conference on Learning Representations","author":"Liu Bingchen","year":"2020","unstructured":"Bingchen Liu, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal. 2020. Towards faster and stabilized GAN training for high-fidelity few-shot image synthesis. In International Conference on Learning Representations."},{"key":"e_1_3_2_82_2","first-page":"6382","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"Liu Kanglin","year":"2019","unstructured":"Kanglin Liu, Wenming Tang, Fei Zhou, and Guoping Qiu. 2019. Spectral regularization for combating mode collapse in GANs. In Proceedings of the IEEE International Conference on Computer Vision. 6382\u20136390."},{"key":"e_1_3_2_83_2","article-title":"Multiclass probabilistic classification vector machine","author":"Lyu Shengfei","year":"2019","unstructured":"Shengfei Lyu, Xing Tian, Yang Li, Bingbing Jiang, and Huanhuan Chen. 2019. Multiclass probabilistic classification vector machine. IEEE Transactions on Neural Networks and Learning Systems 31, 10 (2019), 3906\u20133919.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_84_2","article-title":"How well do WGANs estimate the Wasserstein metric?","author":"Mallasto Anton","year":"2019","unstructured":"Anton Mallasto, Guido Mont\u00fafar, and Augusto Gerolin. 2019. How well do WGANs estimate the Wasserstein metric? arXiv preprint arXiv:1910.03875 (2019).","journal-title":"arXiv preprint arXiv:1910.03875"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1016\/0024-3795(90)90403-Y"},{"key":"e_1_3_2_87_2","article-title":"Which training methods for GANs do actually converge?","author":"Mescheder Lars","year":"2018","unstructured":"Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. 2018. Which training methods for GANs do actually converge? arXiv preprint arXiv:1801.04406 (2018).","journal-title":"arXiv preprint arXiv:1801.04406"},{"key":"e_1_3_2_88_2","first-page":"1825","volume-title":"Advances in Neural Information Processing Systems","author":"Mescheder Lars","year":"2017","unstructured":"Lars Mescheder, Sebastian Nowozin, and Andreas Geiger. 2017. The numerics of GANs. In Advances in Neural Information Processing Systems. 1825\u20131835."},{"key":"e_1_3_2_89_2","article-title":"Spectral normalization for generative adversarial networks","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018. Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018).","journal-title":"arXiv preprint arXiv:1802.05957"},{"key":"e_1_3_2_90_2","article-title":"cGANs with projection discriminator","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato and Masanori Koyama. 2018. cGANs with projection discriminator. arXiv preprint arXiv:1802.05637 (2018).","journal-title":"arXiv preprint arXiv:1802.05637"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"e_1_3_2_92_2","article-title":"McGAN: Mean and covariance feature matching GAN","author":"Mroueh Youssef","year":"2017","unstructured":"Youssef Mroueh, Tom Sercu, and Vaibhava Goel. 2017. McGAN: Mean and covariance feature matching GAN. arXiv preprint arXiv:1702.08398 (2017).","journal-title":"arXiv preprint arXiv:1702.08398"},{"key":"e_1_3_2_93_2","first-page":"5585","volume-title":"Advances in Neural Information Processing Systems","author":"Nagarajan Vaishnavh","year":"2017","unstructured":"Vaishnavh Nagarajan and J. Zico Kolter. 2017. Gradient descent GAN optimization is locally stable. In Advances in Neural Information Processing Systems. 5585\u20135595."},{"key":"e_1_3_2_94_2","article-title":"Towards a better understanding and regularization of GAN training dynamics","author":"Nie Weili","year":"2019","unstructured":"Weili Nie and Ankit Patel. 2019. Towards a better understanding and regularization of GAN training dynamics. arXiv preprint arxiv:1806.09235 (2019).","journal-title":"arXiv preprint arxiv:1806.09235"},{"key":"e_1_3_2_95_2","first-page":"271","volume-title":"Advances in Neural Information Processing Systems","author":"Nowozin Sebastian","year":"2016","unstructured":"Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016. F-GAN: Training generative neural samplers using variational divergence minimization. In Advances in Neural Information Processing Systems. 271\u2013279."},{"key":"e_1_3_2_96_2","article-title":"Is generator conditioning causally related to GAN performance?","author":"Odena Augustus","year":"2018","unstructured":"Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, and Ian Goodfellow. 2018. Is generator conditioning causally related to GAN performance? arXiv preprint arXiv:1802.08768 (2018).","journal-title":"arXiv preprint arXiv:1802.08768"},{"key":"e_1_3_2_97_2","article-title":"Augmented cyclic consistency regularization for unpaired image-to-image translation","author":"Ohkawa Takehiko","year":"2020","unstructured":"Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka, and Nakamasa Inoue. 2020. Augmented cyclic consistency regularization for unpaired image-to-image translation. arXiv preprint arXiv:2003.00187 (2020).","journal-title":"arXiv preprint arXiv:2003.00187"},{"key":"e_1_3_2_98_2","article-title":"Representation learning with contrastive predictive coding","author":"Oord Aaron van den","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).","journal-title":"arXiv preprint arXiv:1807.03748"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000337"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00244"},{"key":"e_1_3_2_101_2","first-page":"3189","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Patel Parth","year":"2021","unstructured":"Parth Patel, Nupur Kumari, Mayank Singh, and Balaji Krishnamurthy. 2021. LT-GAN: Self-supervised GAN with latent transformation detection. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 3189\u20133198."},{"key":"e_1_3_2_102_2","article-title":"On the regularization of Wasserstein GANs","author":"Petzka Henning","year":"2017","unstructured":"Henning Petzka, Asja Fischer, and Denis Lukovnicov. 2017. On the regularization of Wasserstein GANs. arXiv preprint arXiv:1709.08894 (2017).","journal-title":"arXiv preprint arXiv:1709.08894"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01265-2"},{"key":"e_1_3_2_104_2","first-page":"1505","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Qiao Tingting","year":"2019","unstructured":"Tingting Qiao, Jing Zhang, Duanqing Xu, and Dacheng Tao. 2019. Mirrorgan: Learning text-to-image generation by redescription. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1505\u20131514."},{"key":"e_1_3_2_105_2","first-page":"5599","article-title":"Training generative adversarial networks by solving ordinary differential equations","volume":"33","author":"Qin Chongli","year":"2020","unstructured":"Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy Lillicrap, and Pushmeet Kohli. 2020. Training generative adversarial networks by solving ordinary differential equations. Advances in Neural Information Processing Systems 33 (2020), 5599\u20135609.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_106_2","article-title":"Encoding in style: A stylegan encoder for image-to-image translation","author":"Richardson Elad","year":"2020","unstructured":"Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or. 2020. Encoding in style: A stylegan encoder for image-to-image translation. arXiv preprint arXiv:2008.00951 (2020).","journal-title":"arXiv preprint arXiv:2008.00951"},{"key":"e_1_3_2_107_2","first-page":"2018","volume-title":"Advances in Neural Information Processing Systems","author":"Roth Kevin","year":"2017","unstructured":"Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann. 2017. Stabilizing training of generative adversarial networks through regularization. In Advances in Neural Information Processing Systems. 2018\u20132028."},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30671-7_3"},{"key":"e_1_3_2_109_2","first-page":"2234","volume-title":"Advances in Neural Information Processing Systems","author":"Salimans Tim","year":"2016","unstructured":"Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training GANs. In Advances in Neural Information Processing Systems. 2234\u20132242."},{"key":"e_1_3_2_110_2","first-page":"901","volume-title":"Advances in Neural Information Processing Systems","author":"Salimans Tim","year":"2016","unstructured":"Tim Salimans and Durk P. Kingma. 2016. Weight normalization: A simple reparameterization to accelerate training of deep neural networks. In Advances in Neural Information Processing Systems. 901\u2013909."},{"key":"e_1_3_2_111_2","first-page":"1","volume-title":"Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings","author":"Sauer Axel","year":"2022","unstructured":"Axel Sauer, Katja Schwarz, and Andreas Geiger. 2022. StyleGAN-XL: Scaling Stylegan to large diverse datasets. In Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings. 1\u201310."},{"key":"e_1_3_2_112_2","article-title":"Competitive gradient descent","volume":"32","author":"Sch\u00e4fer Florian","year":"2019","unstructured":"Florian Sch\u00e4fer and Anima Anandkumar. 2019. Competitive gradient descent. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00926"},{"key":"e_1_3_2_114_2","article-title":"Simple yet effective way for improving the performance of GAN","author":"Shin Yong-Goo","year":"2019","unstructured":"Yong-Goo Shin, Yoon-Jae Yeo, and Sung-Jea Ko. 2019. Simple yet effective way for improving the performance of GAN. arXiv preprint arXiv:1911.10979 (2019).","journal-title":"arXiv preprint arXiv:1911.10979"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-9735-0_5"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2214488"},{"key":"e_1_3_2_117_2","article-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","author":"Sohn Kihyuk","year":"2020","unstructured":"Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel. 2020. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. arXiv preprint arXiv:2001.07685 (2020).","journal-title":"arXiv preprint arXiv:2001.07685"},{"key":"e_1_3_2_118_2","first-page":"3308","volume-title":"Advances in Neural Information Processing Systems","author":"Srivastava Akash","year":"2017","unstructured":"Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, and Charles Sutton. 2017. VeeGAN: Reducing mode collapse in GANs using implicit variational learning. In Advances in Neural Information Processing Systems. 3308\u20133318."},{"key":"e_1_3_2_119_2","article-title":"Wasserstein GANs work because they fail (to approximate the Wasserstein distance)","author":"Stanczuk Jan","year":"2021","unstructured":"Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser, and Carola-Bibiane Schonlieb. 2021. Wasserstein GANs work because they fail (to approximate the Wasserstein distance). arXiv preprint arXiv:2103.01678 (2021).","journal-title":"arXiv preprint arXiv:2103.01678"},{"key":"e_1_3_2_120_2","article-title":"GAN-QP: A novel GAN framework without gradient vanishing and Lipschitz constraint","author":"Su Jianlin","year":"2018","unstructured":"Jianlin Su. 2018. GAN-QP: A novel GAN framework without gradient vanishing and Lipschitz constraint. arXiv preprint arXiv:1811.07296 (2018).","journal-title":"arXiv preprint arXiv:1811.07296"},{"key":"e_1_3_2_121_2","article-title":"Training generative adversarial networks via turing test","author":"Su Jianlin","year":"2018","unstructured":"Jianlin Su. 2018. Training generative adversarial networks via turing test. arXiv preprint arXiv:1810.10948 (2018).","journal-title":"arXiv preprint arXiv:1810.10948"},{"key":"e_1_3_2_122_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2941272"},{"key":"e_1_3_2_123_2","article-title":"Virtual adversarial Lipschitz regularization","author":"Terj\u00e9k D\u00e1vid","year":"2019","unstructured":"D\u00e1vid Terj\u00e9k. 2019. Virtual adversarial Lipschitz regularization. arXiv preprint arXiv:1907.05681 (2019).","journal-title":"arXiv preprint arXiv:1907.05681"},{"key":"e_1_3_2_124_2","article-title":"Generalization of GANs under Lipschitz continuity and data augmentation","author":"Than Khoat","year":"2021","unstructured":"Khoat Than and Nghia Vu. 2021. Generalization of GANs under Lipschitz continuity and data augmentation. arXiv preprint arXiv:2104.02388 (2021).","journal-title":"arXiv preprint arXiv:2104.02388"},{"key":"e_1_3_2_125_2","article-title":"Improving generalization and stability of generative adversarial networks","author":"Thanh-Tung Hoang","year":"2019","unstructured":"Hoang Thanh-Tung, Truyen Tran, and Svetha Venkatesh. 2019. Improving generalization and stability of generative adversarial networks. arXiv preprint arXiv:1902.03984 (2019).","journal-title":"arXiv preprint arXiv:1902.03984"},{"key":"e_1_3_2_126_2","article-title":"Generative adversarial networks for image super-resolution: A survey","author":"Tian Chunwei","year":"2022","unstructured":"Chunwei Tian, Xuanyu Zhang, Jerry Chun-Wen Lin, Wangmeng Zuo, and Yanning Zhang. 2022. Generative adversarial networks for image super-resolution: A survey. arXiv preprint arXiv:2204.13620 (2022).","journal-title":"arXiv preprint arXiv:2204.13620"},{"key":"e_1_3_2_127_2","first-page":"211","article-title":"Sparse Bayesian learning and the relevance vector machine","volume":"1","author":"Tipping Michael E.","year":"2001","unstructured":"Michael E. Tipping. 2001. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, (June2001), 211\u2013244.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_128_2","first-page":"13253","volume-title":"Advances in Neural Information Processing Systems","author":"Tran Ngoc-Trung","year":"2019","unstructured":"Ngoc-Trung Tran, Viet-Hung Tran, Bao-Ngoc Nguyen, Linxiao Yang, et\u00a0al. 2019. Self-supervised GAN: Analysis and improvement with multi-class minimax game. In Advances in Neural Information Processing Systems. 13253\u201313264."},{"key":"e_1_3_2_129_2","article-title":"Towards good practices for data augmentation in GAN training","author":"Tran Ngoc-Trung","year":"2020","unstructured":"Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Trung-Kien Nguyen, and Ngai-Man Cheung. 2020. Towards good practices for data augmentation in GAN training. arXiv preprint arXiv:2006.05338 (2020).","journal-title":"arXiv preprint arXiv:2006.05338"},{"key":"e_1_3_2_130_2","article-title":"Regularizing generative adversarial networks under limited data","author":"Tseng Hung-Yu","year":"2021","unstructured":"Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, and Weilong Yang. 2021. Regularizing generative adversarial networks under limited data. arXiv preprint arXiv:2104.03310 (2021).","journal-title":"arXiv preprint arXiv:2104.03310"},{"key":"e_1_3_2_131_2","article-title":"Instance normalization: The missing ingredient for fast stylization","author":"Ulyanov Dmitry","year":"2016","unstructured":"Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2016. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016).","journal-title":"arXiv preprint arXiv:1607.08022"},{"key":"e_1_3_2_132_2","first-page":"1058","volume-title":"International Conference on Machine Learning","author":"Wan Li","year":"2013","unstructured":"Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus. 2013. Regularization of neural networks using Dropconnect. In International Conference on Machine Learning. 1058\u20131066."},{"key":"e_1_3_2_133_2","volume-title":"International Joint Conference on Artificial Intelligence (IJCAI\u201917)","author":"Wang Chaoyue","year":"2017","unstructured":"Chaoyue Wang, Chaohui Wang, Chang Xu, and Dacheng Tao. 2017. Tag disentangled generative adversarial networks for object image re-rendering. In International Joint Conference on Artificial Intelligence (IJCAI\u201917)."},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2836316"},{"key":"e_1_3_2_135_2","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2019.2895748"},{"key":"e_1_3_2_136_2","first-page":"5094","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang Yi","year":"2020","unstructured":"Yi Wang, Ying-Cong Chen, Xiangyu Zhang, Jian Sun, and Jiaya Jia. 2020. Attentive normalization for conditional image generation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 5094\u20135103."},{"key":"e_1_3_2_137_2","article-title":"On solving minimax optimization locally: A follow-the-ridge approach","author":"Wang Yuanhao","year":"2019","unstructured":"Yuanhao Wang, Guodong Zhang, and Jimmy Ba. 2019. On solving minimax optimization locally: A follow-the-ridge approach. arXiv preprint arXiv:1910.07512 (2019).","journal-title":"arXiv preprint arXiv:1910.07512"},{"key":"e_1_3_2_138_2","article-title":"Diffusion-GAN: Training GANs with diffusion","author":"Wang Zhendong","year":"2022","unstructured":"Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. 2022. Diffusion-GAN: Training GANs with diffusion. arXiv preprint arXiv:2206.02262 (2022).","journal-title":"arXiv preprint arXiv:2206.02262"},{"key":"e_1_3_2_139_2","article-title":"Improving the improved training of Wasserstein GANs: A consistency term and its dual effect","author":"Wei Xiang","year":"2018","unstructured":"Xiang Wei, Boqing Gong, Zixia Liu, Wei Lu, and Liqiang Wang. 2018. Improving the improved training of Wasserstein GANs: A consistency term and its dual effect. arXiv preprint arXiv:1803.01541 (2018).","journal-title":"arXiv preprint arXiv:1803.01541"},{"key":"e_1_3_2_140_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.08.007"},{"key":"e_1_3_2_141_2","article-title":"View Vertically: A hierarchical network for trajectory prediction via fourier spectrums","author":"Wong Conghao","year":"2021","unstructured":"Conghao Wong, Beihao Xia, Ziming Hong, Qinmu Peng, Wei Yuan, Qiong Cao, Yibo Yang, and Xinge You. 2021. View Vertically: A hierarchical network for trajectory prediction via fourier spectrums. arXiv preprint arXiv:2110.07288 (2021).","journal-title":"arXiv preprint arXiv:2110.07288"},{"key":"e_1_3_2_142_2","first-page":"653","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Wu Jiqing","year":"2018","unstructured":"Jiqing Wu, Zhiwu Huang, Janine Thoma, Dinesh Acharya, and Luc Van Gool. 2018. Wasserstein divergence for GANs. In Proceedings of the European Conference on Computer Vision. 653\u2013668."},{"key":"e_1_3_2_143_2","first-page":"3","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201918)","author":"Wu Yuxin","year":"2018","unstructured":"Yuxin Wu and Kaiming He. 2018. Group normalization. In Proceedings of the European Conference on Computer Vision (ECCV\u201918). 3\u201319."},{"key":"e_1_3_2_144_2","article-title":"Improving GAN training with probability ratio clipping and sample reweighting","author":"Wu Yue","year":"2020","unstructured":"Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, and Zhiting Hu. 2020. Improving GAN training with probability ratio clipping and sample reweighting. arXiv preprint arXiv:2006.06900 (2020).","journal-title":"arXiv preprint arXiv:2006.06900"},{"key":"e_1_3_2_145_2","first-page":"6373","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Wu Yi-Lun","year":"2021","unstructured":"Yi-Lun Wu, Hong-Han Shuai, Zhi-Rui Tam, and Hong-Yu Chiu. 2021. Gradient normalization for generative adversarial networks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 6373\u20136382."},{"key":"e_1_3_2_146_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108552"},{"key":"e_1_3_2_147_2","article-title":"On the effects of batch and weight normalization in generative adversarial networks","author":"Xiang Sitao","year":"2017","unstructured":"Sitao Xiang and Hao Li. 2017. On the effects of batch and weight normalization in generative adversarial networks. arXiv preprint arXiv:1704.03971 (2017).","journal-title":"arXiv preprint arXiv:1704.03971"},{"key":"e_1_3_2_148_2","article-title":"Real or not real, that is the question","author":"Xiangli Yuanbo","year":"2020","unstructured":"Yuanbo Xiangli, Yubin Deng, Bo Dai, Chen Change Loy, and Dahua Lin. 2020. Real or not real, that is the question. arXiv preprint arXiv:2002.05512 (2020).","journal-title":"arXiv preprint arXiv:2002.05512"},{"key":"e_1_3_2_149_2","article-title":"Unsupervised data augmentation for consistency training","author":"Xie Qizhe","year":"2019","unstructured":"Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V. Le. 2019. Unsupervised data augmentation for consistency training. arXiv preprint arXiv:1904.12848 (2019).","journal-title":"arXiv preprint arXiv:1904.12848"},{"key":"e_1_3_2_150_2","unstructured":"Minkai Xu Zhiming Zhou Guansong Lu Jian Tang Weinan Zhang and Yong Yu. 2021. Towards Generalized Implementation of Wasserstein Distance in GANs. (2021). arxiv:cs.LG\/2012.03420"},{"key":"e_1_3_2_151_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00143"},{"key":"e_1_3_2_152_2","article-title":"Stabilizing adversarial nets with prediction methods","author":"Yadav Abhay","year":"2017","unstructured":"Abhay Yadav, Sohil Shah, Zheng Xu, David Jacobs, and Tom Goldstein. 2017. Stabilizing adversarial nets with prediction methods. arXiv preprint arXiv:1705.07364 (2017).","journal-title":"arXiv preprint arXiv:1705.07364"},{"key":"e_1_3_2_153_2","first-page":"9378","article-title":"Data-efficient instance generation from instance discrimination","volume":"34","author":"Yang Ceyuan","year":"2021","unstructured":"Ceyuan Yang, Yujun Shen, Yinghao Xu, and Bolei Zhou. 2021. Data-efficient instance generation from instance discrimination. Advances in Neural Information Processing Systems 34 (2021), 9378\u20139390.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_154_2","article-title":"Diversity-sensitive conditional generative adversarial networks","author":"Yang Dingdong","year":"2019","unstructured":"Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, and Honglak Lee. 2019. Diversity-sensitive conditional generative adversarial networks. arXiv preprint arXiv:1901.09024 (2019).","journal-title":"arXiv preprint arXiv:1901.09024"},{"key":"e_1_3_2_155_2","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1109\/ICIP40778.2020.9191083","volume-title":"2020 IEEE International Conference on Image Processing (ICIP\u201920)","author":"Yazici Yasin","year":"2020","unstructured":"Yasin Yazici, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, and Vijay Chandrasekhar. 2020. Empirical analysis of overfitting and mode drop in GAN training. In 2020 IEEE International Conference on Image Processing (ICIP\u201920). IEEE, 1651\u20131655."},{"key":"e_1_3_2_156_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101552"},{"key":"e_1_3_2_157_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00577"},{"key":"e_1_3_2_158_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"e_1_3_2_159_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00156"},{"key":"e_1_3_2_160_2","article-title":"mixup: Beyond empirical risk minimization","author":"Zhang Hongyi","year":"2017","unstructured":"Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. 2017. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017).","journal-title":"arXiv preprint arXiv:1710.09412"},{"key":"e_1_3_2_161_2","article-title":"Self-attention generative adversarial networks","author":"Zhang Han","year":"2018","unstructured":"Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. 2018. Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318 (2018).","journal-title":"arXiv preprint arXiv:1805.08318"},{"key":"e_1_3_2_162_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.629"},{"key":"e_1_3_2_163_2","article-title":"Consistency regularization for generative adversarial networks","author":"Zhang Han","year":"2019","unstructured":"Han Zhang, Zizhao Zhang, Augustus Odena, and Honglak Lee. 2019. Consistency regularization for generative adversarial networks. arXiv preprint arXiv:1910.12027 (2019).","journal-title":"arXiv preprint arXiv:1910.12027"},{"key":"e_1_3_2_164_2","article-title":"A Wasserstein GAN model with the total variational regularization","author":"Zhang Lijun","year":"2018","unstructured":"Lijun Zhang, Yujin Zhang, and Yongbin Gao. 2018. A Wasserstein GAN model with the total variational regularization. arXiv preprint arXiv:1812.00810 (2018).","journal-title":"arXiv preprint arXiv:1812.00810"},{"key":"e_1_3_2_165_2","article-title":"Spectral bounding: Strictly satisfying the 1-Lipschitz property for generative adversarial networks","author":"Zhang Zhihong","year":"2019","unstructured":"Zhihong Zhang, Yangbin Zeng, Lu Bai, Yiqun Hu, Meihong Wu, Shuai Wang, and Edwin R. Hancock. 2019. Spectral bounding: Strictly satisfying the 1-Lipschitz property for generative adversarial networks. Pattern Recognition 105 (2020), 107179.","journal-title":"Pattern Recognition"},{"key":"e_1_3_2_166_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2019.2959886"},{"key":"e_1_3_2_167_2","article-title":"Large scale image completion via co-modulated generative adversarial networks","author":"Zhao Shengyu","year":"2021","unstructured":"Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang, Eric I. Chang, and Yan Xu. 2021. Large scale image completion via co-modulated generative adversarial networks. arXiv preprint arXiv:2103.10428 (2021).","journal-title":"arXiv preprint arXiv:2103.10428"},{"key":"e_1_3_2_168_2","article-title":"Differentiable augmentation for data-efficient gan training","volume":"33","author":"Zhao Shengyu","year":"2020","unstructured":"Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han. 2020. Differentiable augmentation for data-efficient gan training. Advances in Neural Information Processing Systems 33 (2020), 7559\u20137570.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_169_2","article-title":"Bias and generalization in deep generative models: An empirical study","author":"Zhao Shengjia","year":"2018","unstructured":"Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, and Stefano Ermon. 2018. Bias and generalization in deep generative models: An empirical study. arXiv preprint arXiv:1811.03259 (2018).","journal-title":"arXiv preprint arXiv:1811.03259"},{"key":"e_1_3_2_170_2","article-title":"Improved consistency regularization for GANs","author":"Zhao Zhengli","year":"2020","unstructured":"Zhengli Zhao, Sameer Singh, Honglak Lee, Zizhao Zhang, Augustus Odena, and Han Zhang. 2020. Improved consistency regularization for GANs. arXiv preprint arXiv:2002.04724 (2020).","journal-title":"arXiv preprint arXiv:2002.04724"},{"key":"e_1_3_2_171_2","article-title":"Image augmentations for GAN training","author":"Zhao Zhengli","year":"2020","unstructured":"Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, and Han Zhang. 2020. Image augmentations for GAN training. arXiv preprint arXiv:2006.02595 (2020).","journal-title":"arXiv preprint arXiv:2006.02595"},{"key":"e_1_3_2_172_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.08.004"},{"key":"e_1_3_2_173_2","first-page":"8040","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"Zhou Sanping","year":"2019","unstructured":"Sanping Zhou, Fei Wang, Zeyi Huang, and Jinjun Wang. 2019. Discriminative feature learning with consistent attention regularization for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision. 8040\u20138049."},{"key":"e_1_3_2_174_2","article-title":"Lipschitz generative adversarial nets","author":"Zhou Zhiming","year":"2019","unstructured":"Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, and Zhihua Zhang. 2019. Lipschitz generative adversarial nets. arXiv preprint arXiv:1902.05687 (2019).","journal-title":"arXiv preprint arXiv:1902.05687"},{"key":"e_1_3_2_175_2","article-title":"Towards efficient and unbiased implementation of Lipschitz continuity in GANs","author":"Zhou Zhiming","year":"2019","unstructured":"Zhiming Zhou, Jian Shen, Yuxuan Song, Weinan Zhang, and Yong Yu. 2019. 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