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• W4311212916 abstract "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 facial GANs , 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." @default.
• W4311212916 created "2022-12-24" @default.
• W4311212916 creator A5010455422 @default.
• W4311212916 creator A5026522015 @default.
• W4311212916 creator A5065642961 @default.
• W4311212916 creator A5069092245 @default.
• W4311212916 creator A5078017466 @default.
• W4311212916 date "2022-12-03" @default.
• W4311212916 modified "2023-09-30" @default.
• W4311212916 title "Generative Adversarial Networks for Face Generation: A Survey" @default.
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