Title Exploring generative adversarial networks: comparative analysis of facial image synthesis and the extension of creative capacities in artificial intelligence /
Authors Eglynas, Tomas ; Lizdenis, Dovydas ; Raudys, Aistis ; Jakovlev, Sergej ; Voznak, Miroslav
DOI 10.1109/ACCESS.2025.3531726
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Is Part of IEEE Access.. Piscataway : Institute of Electrical and Electronics Engineers Inc.. 2025, vol. 13, p. 19588-19597.. eISSN 2169-3536
Keywords [eng] computer graphics ; Human image synthesis ; image processing ; photorealism ; visualization
Abstract [eng] Neural networks have become foundational in modern technology, driving advancements across diverse domains such as medicine, law enforcement, and information technology. By enabling algorithms to learn from data and perform tasks autonomously, they eliminate the need for explicit programming. A significant challenge in this field is replicating the uniquely human capacity for creativity - envisioning and realizing novel concepts and tangible creations. Generative Adversarial Networks (GANs), a leading approach in this effort, are especially notable for synthesizing realistic human facial images. Despite the success of GANs, comprehensive comparative studies of face-generating GAN methodologies are limited. This paper addresses this gap by analyzing the scope and capabilities of facial generation, detailing the principles of the original GAN framework, and reviewing prominent GAN variants specifically designed for facial synthesis. Through performance evaluations and fidelity analysis of generated images, this study contributes to a deeper understanding of GAN potential in advancing artificial intelligence creativity through performance evaluations and fidelity analysis of generated images.
Published Piscataway : Institute of Electrical and Electronics Engineers Inc
Type Journal article
Language English
Publication date 2025
CC license CC license description