GAN-based image generation

Generative Adversarial Networks (GANs), as a deep learning model, have made significant progress in the field of image generation and style migration. This study aims to methodically investigate GAN-based image generation methods. First, this paper outlines the basic principles of GAN and its application in image generation, focusing on analyzing the structure and performance of representative models such as DCGAN, ProGAN and StyleGAN. This paper summarizes the improvement methods such as WGAN and LSGAN, and evaluates their efficacy in improving the stability of the model and the quality of the generated images in view of the problems of pattern collapse and instability faced by GANs in the training process. Finally, this paper discusses the limitations of current techniques and possible future directions, and suggests research prospects in the field of multimodal fusion and 3D image generation. The research in this paper provides a theoretical framework and useful suggestions for enhancing the application of GAN methods in image generation.