{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T21:03:38Z","timestamp":1792184618029,"version":"4.4.0"},"reference-count":169,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T00:00:00Z","timestamp":1670025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>\n            Recently, generative adversarial networks (GANs) have progressed enormously, which makes them able to learn complex data distributions in particular faces. More and more efficient GAN architectures have been designed and proposed to learn the different variations of faces, such as cross pose, age, expression, and style. These GAN-based approaches need to be reviewed, discussed, and categorized in terms of architectures, applications, and metrics. Several reviews that focus on the use and advances of GAN in general have been proposed. However, to the best of our knowledge, the GAN models applied to the face, which we call\n            <jats:italic>facial GANs<\/jats:italic>\n            , have never been addressed. In this article, we review facial GANs and their different applications. We mainly focus on architectures, problems, and performance evaluation with respect to each application and used datasets. More precisely, we review the progress of architectures and discuss the contributions and limits of each. Then, we expose the encountered problems of facial GANs and propose solutions to handle them. Additionally, as GAN evaluation has become a notable current defiance, we investigate the state-of-the-art quantitative and qualitative evaluation metrics and their applications. We conclude this work with a discussion on the face generation challenges and propose open research issues.\n          <\/jats:p>","DOI":"10.1145\/3527850","type":"journal-article","created":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T10:20:44Z","timestamp":1670062844000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":135,"title":["Generative Adversarial Networks for Face Generation: A Survey"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9829-2421","authenticated-orcid":false,"given":"Amina","family":"Kammoun","sequence":"first","affiliation":[{"name":"University of Sfax, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2723-9874","authenticated-orcid":false,"given":"Rim","family":"Slama","sequence":"additional","affiliation":[{"name":"CESI Lyon, Lyon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1827-7150","authenticated-orcid":false,"given":"Hedi","family":"Tabia","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2605-0674","authenticated-orcid":false,"given":"Tarek","family":"Ouni","sequence":"additional","affiliation":[{"name":"University of Sfax, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7409-292X","authenticated-orcid":false,"given":"Mohmed","family":"Abid","sequence":"additional","affiliation":[{"name":"University of Sfax, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,12,3]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09835-4"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jjimei.2020.100004"},{"key":"e_1_3_2_4_2","volume-title":"Proceedings of the International Conference on Image Analysis and Processing (ICIAP\u201919)","author":"Alqahtani Hamed","year":"2019","unstructured":"Hamed Alqahtani and Manolya Kavakli-Thorne. 2019. 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A review on generative adversarial networks: Algorithms, theory, and applications. arXiv preprint arXiv:2001.06937 (2020).","journal-title":"arXiv preprint arXiv:2001.06937"},{"key":"e_1_3_2_51_2","article-title":"Improved training of Wasserstein GANs","author":"Gulrajani Ishaan","year":"2017","unstructured":"Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. 2017. Improved training of Wasserstein GANs. arXiv preprint arXiv:1704.00028 (2017).","journal-title":"arXiv preprint arXiv:1704.00028"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2019.102805"},{"key":"e_1_3_2_53_2","article-title":"GANSpace: Discovering interpretable gan controls","author":"H\u00e4rk\u00f6nen Erik","year":"2020","unstructured":"Erik H\u00e4rk\u00f6nen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris. 2020. GANSpace: Discovering interpretable gan controls. arXiv preprint arXiv:2004.02546 (2020).","journal-title":"arXiv preprint arXiv:2004.02546"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2019.2916751"},{"key":"e_1_3_2_55_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_56_2","article-title":"Comparative study on generative adversarial networks","author":"Hitawala Saifuddin","year":"2018","unstructured":"Saifuddin Hitawala. 2018. Comparative study on generative adversarial networks. arXiv preprint arXiv:1801.04271 (2018).","journal-title":"arXiv preprint arXiv:1801.04271"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3301282"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACII.2019.8925461"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00876"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.267"},{"key":"e_1_3_2_61_2","article-title":"FakeLocator: Robust localization of GAN-based face manipulations via semantic segmentation networks with bells and whistles","author":"Huang Yihao","year":"2020","unstructured":"Yihao Huang, Felix Juefei-Xu, Run Wang, Xiaofei Xie, Lei Ma, Jianwen Li, Weikai Miao, Yang Liu, and Geguang Pu. 2020. FakeLocator: Robust localization of GAN-based face manipulations via semantic segmentation networks with bells and whistles. arXiv preprint arXiv:2001.09598 (2020).","journal-title":"arXiv preprint arXiv:2001.09598"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_3_2_63_2","article-title":"A survey on generative adversarial networks: Variants, applications, and training","author":"Jabbar Abdul","year":"2020","unstructured":"Abdul Jabbar, Xi Li, and Bourahla Omar. 2020. A survey on generative adversarial networks: Variants, applications, and training. arXiv preprint arXiv:2006.05132 (2020).","journal-title":"arXiv preprint arXiv:2006.05132"},{"key":"e_1_3_2_64_2","article-title":"CycleGAN face-off","author":"Jin Xiaohan","year":"2017","unstructured":"Xiaohan Jin, Ye Qi, and Shangxuan Wu. 2017. CycleGAN face-off. arXiv preprint arXiv:1712.03451 (2017).","journal-title":"arXiv preprint arXiv:1712.03451"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00782"},{"key":"e_1_3_2_66_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_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.23919\/BIOSIG.2018.8552937"},{"key":"e_1_3_2_70_2","article-title":"Deep generative-contrastive networks for facial expression recognition","author":"Kim Youngsung","year":"2017","unstructured":"Youngsung Kim, ByungIn Yoo, Youngjun Kwak, Changkyu Choi, and Junmo Kim. 2017. Deep generative-contrastive networks for facial expression recognition. arXiv preprint arXiv:1703.07140 (2017).","journal-title":"arXiv preprint arXiv:1703.07140"},{"key":"e_1_3_2_71_2","article-title":"Glow: Generative flow with invertible 1x1 convolutions","author":"Kingma Diederik P.","year":"2018","unstructured":"Diederik P. Kingma and Prafulla Dhariwal. 2018. Glow: Generative flow with invertible 1x1 convolutions. arXiv preprint arXiv:1807.03039 (2018).","journal-title":"arXiv preprint arXiv:1807.03039"},{"key":"e_1_3_2_72_2","article-title":"Auto-encoding variational Bayes","author":"Kingma Diederik P.","year":"2013","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013).","journal-title":"arXiv preprint arXiv:1312.6114"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992934"},{"key":"e_1_3_2_74_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_75_2","first-page":"3581","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Kurach Karol","year":"2019","unstructured":"Karol Kurach, Mario Lu\u010di\u0107, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly. 2019. A large-scale study on regularization and normalization in GANs. In Proceedings of the International Conference on Machine Learning. 3581\u20133590."},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2018.00046"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00084"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"e_1_3_2_79_2","article-title":"Harmonizing maximum likelihood with GANs for multimodal conditional generation","author":"Lee Soochan","year":"2019","unstructured":"Soochan Lee, Junsoo Ha, and Gunhee Kim. 2019. Harmonizing maximum likelihood with GANs for multimodal conditional generation. arXiv preprint arXiv:1902.09225 (2019).","journal-title":"arXiv preprint arXiv:1902.09225"},{"key":"e_1_3_2_80_2","article-title":"Deep facial expression recognition: A survey","author":"Li Shan","year":"2020","unstructured":"Shan Li and Weihong Deng. 2020. Deep facial expression recognition: A survey. IEEE Transactions on Affective Computing. Early access, March 17, 2020.","journal-title":"IEEE Transactions on Affective Computing."},{"key":"e_1_3_2_81_2","unstructured":"Xiaoyi Li and Xiaowen Yu. n.d. Generating Cartoon Style Facial Expressions with StackGAN . Stanford University."},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/APSIPAASC47483.2019.9023144"},{"key":"e_1_3_2_83_2","unstructured":"David Liu and Nathan Hu. 2020. GAN-Based Image Data Augmentation . Stanford University."},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3033675"},{"key":"e_1_3_2_85_2","article-title":"X-GANs: Image reconstruction made easy for extreme cases","author":"Liu Longfei","year":"2018","unstructured":"Longfei Liu, Sheng Li, Yisong Chen, and Guoping Wang. 2018. X-GANs: Image reconstruction made easy for extreme cases. arXiv preprint arXiv:1808.04432 (2018).","journal-title":"arXiv preprint arXiv:1808.04432"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00379"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3049196"},{"key":"e_1_3_2_88_2","article-title":"Coupled generative adversarial networks","author":"Liu Ming-Yu","year":"2016","unstructured":"Ming-Yu Liu and Oncel Tuzel. 2016. Coupled generative adversarial networks. arXiv preprint arXiv:1606.07536 (2016).","journal-title":"arXiv preprint arXiv:1606.07536"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.104093"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01215"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01047"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_18"},{"key":"e_1_3_2_93_2","article-title":"Are GANs created equal? A large-scale study","author":"Lucic Mario","year":"2017","unstructured":"Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet. 2017. Are GANs created equal? 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In Proceedings of the International Conference on Smart Multimedia. 439\u2013450."},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00388"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-25614-2_11"},{"key":"e_1_3_2_98_2","article-title":"Unpaired multi-domain image generation via regularized conditional GANs","author":"Mao Xudong","year":"2018","unstructured":"Xudong Mao and Qing Li. 2018. Unpaired multi-domain image generation via regularized conditional GANs. arXiv preprint arXiv:1805.02456 (2018).","journal-title":"arXiv preprint arXiv:1805.02456"},{"key":"e_1_3_2_99_2","article-title":"A 3D GAN for improved large-pose facial recognition","author":"Marriott Richard T.","year":"2020","unstructured":"Richard T. Marriott, Sami Romdhani, and Liming Chen. 2020. A 3D GAN for improved large-pose facial recognition. arXiv preprint arXiv:2012.10545 (2020).","journal-title":"arXiv preprint arXiv:2012.10545"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1109\/SIBGRAPI.2018.00067"},{"key":"e_1_3_2_101_2","article-title":"Conditional generative adversarial nets","author":"Mirza Mehdi","year":"2014","unstructured":"Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).","journal-title":"arXiv preprint arXiv:1411.1784"},{"key":"e_1_3_2_102_2","article-title":"Unsupervised depth estimation, 3D face rotation and replacement","author":"Moniz Joel Ruben Antony","year":"2018","unstructured":"Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, and Christopher Pal. 2018. Unsupervised depth estimation, 3D face rotation and replacement. arXiv preprint arXiv:1803.09202 (2018).","journal-title":"arXiv preprint arXiv:1803.09202"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01329-8"},{"key":"e_1_3_2_104_2","first-page":"7588","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Nguyen-Phuoc Thu","year":"2019","unstructured":"Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang. 2019. HoloGAN: Unsupervised learning of 3D representations from natural images. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 7588\u20137597."},{"key":"e_1_3_2_105_2","first-page":"353","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201918)","author":"Nimisha Thekke Madam","year":"2018","unstructured":"Thekke Madam Nimisha, Kumar Sunil, and A. N. Rajagopalan. 2018. Unsupervised class-specific deblurring. In Proceedings of the European Conference on Computer Vision (ECCV\u201918). 353\u2013369."},{"key":"e_1_3_2_106_2","first-page":"2642","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Odena Augustus","year":"2017","unstructured":"Augustus Odena, Christopher Olah, and Jonathon Shlens. 2017. Conditional image synthesis with auxiliary classifier gans. In Proceedings of the International Conference on Machine Learning. 2642\u20132651."},{"key":"e_1_3_2_107_2","article-title":"GANGs: Generative adversarial network games","author":"Oliehoek Frans A.","year":"2017","unstructured":"Frans A. Oliehoek, Rahul Savani, Jose Gallego-Posada, Elise Van der Pol, Edwin D. De Jong, and Roderich Gro\u00df. 2017. GANGs: Generative adversarial network games. arXiv preprint arXiv:1712.00679 (2017).","journal-title":"arXiv preprint arXiv:1712.00679"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2019.06.004"},{"key":"e_1_3_2_109_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2020.2991774"},{"key":"e_1_3_2_110_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2905015"},{"key":"e_1_3_2_111_2","article-title":"Invertible conditional GANs for image editing","author":"Perarnau Guim","year":"2016","unstructured":"Guim Perarnau, Joost Van De Weijer, Bogdan Raducanu, and Jose M. \u00c1lvarez. 2016. Invertible conditional GANs for image editing. arXiv preprint arXiv:1611.06355 (2016).","journal-title":"arXiv preprint arXiv:1611.06355"},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01013"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01008"},{"key":"e_1_3_2_114_2","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford Alec","year":"2015","unstructured":"Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015).","journal-title":"arXiv preprint arXiv:1511.06434"},{"key":"e_1_3_2_115_2","article-title":"Do generative models know disentanglement? Contrastive learning is all you need","author":"Ren Xuanchi","year":"2021","unstructured":"Xuanchi Ren, Tao Yang, Yuwang Wang, and Wenjun Zeng. 2021. Do generative models know disentanglement? Contrastive learning is all you need. arXiv preprint arXiv:2102.10543 (2021).","journal-title":"arXiv preprint arXiv:2102.10543"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2986079"},{"key":"e_1_3_2_117_2","article-title":"Stabilizing training of generative adversarial networks through regularization","author":"Roth Kevin","year":"2017","unstructured":"Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann. 2017. Stabilizing training of generative adversarial networks through regularization. arXiv preprint arXiv:1705.09367 (2017).","journal-title":"arXiv preprint arXiv:1705.09367"},{"key":"e_1_3_2_118_2","article-title":"Improved techniques for training GANs","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. arXiv preprint arXiv:1606.03498 (2016).","journal-title":"arXiv preprint arXiv:1606.03498"},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1145\/3446374"},{"key":"e_1_3_2_120_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.02.014"},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3034267"},{"key":"e_1_3_2_122_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01288-9"},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.5555\/3304415.3304543"},{"key":"e_1_3_2_124_2","article-title":"Semi-supervised conditional GANs","author":"Sricharan Kumar","year":"2017","unstructured":"Kumar Sricharan, Raja Bala, Matthew Shreve, Hui Ding, Kumar Saketh, and Jin Sun. 2017. Semi-supervised conditional GANs. arXiv preprint arXiv:1708.05789 (2017).","journal-title":"arXiv preprint arXiv:1708.05789"},{"key":"e_1_3_2_125_2","volume-title":"Proceedings of the Asian Conference on Computer Vision","author":"Sun Jiaze","year":"2020","unstructured":"Jiaze Sun, Binod Bhattarai, and Tae-Kyun Kim. 2020. MatchGAN: A self-supervised semi-supervised conditional generative adversarial network. In Proceedings of the Asian Conference on Computer Vision."},{"key":"e_1_3_2_126_2","unstructured":"Gino Tesei. 2019. Generating realistic facial expressions through conditional cycle-consistent generative adversarial networks (CCycleGAN). Project for CS230 Deep Learning."},{"key":"e_1_3_2_127_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104119"},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.141"},{"key":"e_1_3_2_129_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2868350"},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093474"},{"key":"e_1_3_2_131_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00137"},{"key":"e_1_3_2_132_2","first-page":"9786","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Voynov Andrey","year":"2020","unstructured":"Andrey Voynov and Artem Babenko. 2020. Unsupervised discovery of interpretable directions in the GAN latent space. In Proceedings of the International Conference on Machine Learning. 9786\u20139796."},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-23367-9"},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2017.7510583"},{"key":"e_1_3_2_135_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.11.020"},{"key":"e_1_3_2_136_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2978633"},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00917"},{"key":"e_1_3_2_138_2","article-title":"A survey on face data augmentation","author":"Wang Xiang","year":"2019","unstructured":"Xiang Wang, Kai Wang, and Shiguo Lian. 2019. A survey on face data augmentation. arXiv preprint arXiv:1904.11685 (2019).","journal-title":"arXiv preprint arXiv:1904.11685"},{"key":"e_1_3_2_139_2","article-title":"Generative adversarial networks in computer vision: A survey and taxonomy","author":"Wang Zhengwei","year":"2019","unstructured":"Zhengwei Wang, Qi She, and Tomas E. Ward. 2019. Generative adversarial networks in computer vision: A survey and taxonomy. arXiv preprint arXiv:1906.01529 (2019).","journal-title":"arXiv preprint arXiv:1906.01529"},{"key":"e_1_3_2_140_2","article-title":"3D face image inpainting with generative adversarial nets","volume":"2020","author":"Wei Tongxin","year":"2020","unstructured":"Tongxin Wei, Qingbao Li, Jinjin Liu, Ping Zhang, and Zhifeng Chen. 2020. 3D face image inpainting with generative adversarial nets. Mathematical Problems in Engineering 2020, 6 (2020), 1\u201311.","journal-title":"Mathematical Problems in Engineering"},{"key":"e_1_3_2_141_2","article-title":"Stabilizing generative adversarial networks: A survey","author":"Wiatrak Maciej","year":"2019","unstructured":"Maciej Wiatrak, Stefano V. Albrecht, and Andrew Nystrom. 2019. Stabilizing generative adversarial networks: A survey. arXiv preprint arXiv:1910.00927 (2019).","journal-title":"arXiv preprint arXiv:1910.00927"},{"key":"e_1_3_2_142_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_41"},{"key":"e_1_3_2_143_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00622"},{"key":"e_1_3_2_144_2","article-title":"FaceController: Controllable attribute editing for face in the wild","author":"Xu Zhiliang","year":"2021","unstructured":"Zhiliang Xu, Xiyu Yu, Zhibin Hong, Zhen Zhu, Junyu Han, Jingtuo Liu, Errui Ding, and Xiang Bai. 2021. FaceController: Controllable attribute editing for face in the wild. arXiv preprint arXiv:2102.11464 (2021).","journal-title":"arXiv preprint arXiv:2102.11464"},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00231"},{"key":"e_1_3_2_146_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00011"},{"key":"e_1_3_2_147_2","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2018.00050"},{"key":"e_1_3_2_148_2","doi-asserted-by":"publisher","DOI":"10.23919\/PIERS.2018.8598226"},{"key":"e_1_3_2_149_2","first-page":"676","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Yim Junho","year":"2015","unstructured":"Junho Yim, Heechul Jung, ByungIn Yoo, Changkyu Choi, Dusik Park, and Junmo Kim. 2015. Rotating your face using multi-task deep neural network. 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ST-GAN: Unsupervised facial image semantic transformation using generative adversarial networks. In Proceedings of the Asian Conference on Machine Learning. 248\u2013263."},{"key":"e_1_3_2_158_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58529-7_38"},{"key":"e_1_3_2_159_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"e_1_3_2_160_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2019.03.010"},{"key":"e_1_3_2_161_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.463"},{"key":"e_1_3_2_162_2","article-title":"Unsupervised adversarial attacks on deep feature-based retrieval with GAN","author":"Zhao Guoping","year":"2019","unstructured":"Guoping Zhao, Mingyu Zhang, Jiajun Liu, and Ji-Rong Wen. 2019. Unsupervised adversarial attacks on deep feature-based retrieval with GAN. arXiv preprint arXiv:1907.05793 (2019).","journal-title":"arXiv preprint arXiv:1907.05793"},{"key":"e_1_3_2_163_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019251"},{"key":"e_1_3_2_164_2","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294778"},{"key":"e_1_3_2_165_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858819"},{"key":"e_1_3_2_166_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00595"},{"key":"e_1_3_2_167_2","article-title":"Hype: A benchmark for human eye perceptual evaluation of generative models","author":"Zhou Sharon","year":"2019","unstructured":"Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, and Michael S. Bernstein. 2019. 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