A Study of Image Generation Methods that are based on Deep Learning

Abstract

In recent years, significant advancements have been made in the field of deep learning-driven image generation technology. Initially, the technology was characterized by the generation of low-quality image samples. However, it has since evolved to produce highly realistic and diverse images, which have found widespread applications in domains such as computer vision, art creation, medical imaging, and virtual reality. In this paper, we systematically study the current mainstream deep generative models, including Generative Adversarial Networks (GAN), Variational Auto-Encoders (VAE), and Diffusion Models. We focus on their core principles, representative results, performance characteristics, and application scenarios. We also analyze the strengths and shortcomings of the various models in depth. The paper undertakes a systematic comparison and summarization of the current state of research in the field, identifying the predominant challenges and anticipating future developments. The objective is to furnish researchers in the domain of image generation with a coherent technological trajectory and a comprehensive theoretical framework.