Research and application of generative adversarial networks model in computer vision

Generative Adversarial Networks (GAN), recent advancements in computer vision have been greatly influenced by the innovative progress of deep learning techniques. GAN can generate realistic and diverse multimedia content such as images and videos through the adversarial training mechanism of generators and discriminators, it is extensively applied in tasks like image creation, enhancement, and manipulation. This paper offers a comprehensive exploration of the core structure of GAN models and their uses in computer vision, exploring their advantages and challenges in tasks such as image generation, restoration, and transformation. The experimental section employs the CIFAR-10 and CelebA datasets to compare GAN-based models (DCGAN, SN-GAN, SAGAN) with traditional methods (VAE, PixelCNN) in terms of image generation quality, generation efficiency, and model stability. The findings indicate that models based on GANs outperform conventional techniques in both the quality and diversity of generated images. Enhanced models like SN-GAN and SAGAN exhibit better performance in terms of accuracy, Inception Score (IS), and Fréchet Inception Distance (FID) metrics. Despite longer training times, GAN-based models exhibit a significant advantage in generation speed. This research offers useful references and perspectives for the continued use and advancement of GANs in the computer vision domain.