Research Progress of Image Generation Methods Based on Deep Learning

This paper provides an overview of deep learning-based image generation methods and image style migration techniques. The focus is on text-to-image generation, unconditional and conditional image generation methods, and various approaches for image style migration. The review covers the underlying models, including Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), and diffusion models, while also discussing conditional image generation methods such as Conditional Generative Adversarial Networks (CGAN) and Stacked Generative Adversarial Network (StackGAN). Furthermore, it explores deep learning-based image style migration methods, including both image iteration-based and model iteration-based approaches. The possibilities and opportunities for the advancement and use of these techniques in the field of picture production are highlighted in the review's conclusion.