Abstract:
This paper presents an innovative approach to enhancing Generative Adversarial Networks (GANs) for human face image generation. Generative tasks are traditionally hindere...Show MoreMetadata
Abstract:
This paper presents an innovative approach to enhancing Generative Adversarial Networks (GANs) for human face image generation. Generative tasks are traditionally hindered by issues like mode collapse and unstable training. We introduce a groundbreaking integration of Nash equilibrium principles into GAN architectures, featuring a tailored loss function that significantly stabilizes the training process. Our method not only bolsters GAN stability but also substantially improves the quality and variety of generated expressions. Rigorous experimentation across benchmark datasets like CIFAR-10, CelebA, and ImageNet confirms the exceptional performance of our model, particularly evidenced by significant reductions in the Fréchet Inception Distance, a testament to the method’s efficacy in producing more realistic and diverse facial expressions. The implications of this research are far-reaching, particularly in the realm of human-machine interaction where expressive and diverse facial expressions are crucial. We will extend the application of the proposed method to other domains beyond facial expression generation, such as medical imaging or autonomous vehicle perception systems, to assess its adaptability and effectiveness.
Date of Conference: 30 June 2024 - 05 July 2024
Date Added to IEEE Xplore: 09 September 2024
ISBN Information: