Research and Analysis of VAE, GAN, and Diffusion Generation Models

Abstract

Generative models constitute a vital component of machine learning, having evolved to find extensive application in both image processing and natural language processing domains. This paper systematically reviews the three predominant generative models: Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Diffusion Models (DM). It provides a comprehensive analysis of their fundamental principles, respective strengths and limitations, alongside their practical applications. From the aforementioned generative approaches, it is evident that: the VAE method innovatively incorporates probabilistic distributions to enable controlled sampling, though the generated quality tends to be somewhat blurred; the GAN method employs a generativeadversarial framework, yielding higher-quality images, yet numerous challenges persist, such as training instability; Diffusion models employ a forward noise addition and reverse denoising approach, effectively addressing both aforementioned issues. They demonstrate strong pattern coverage capabilities and excellent stability, though they suffer from slow generation speeds and high computational demands. This comparative analysis provides valuable reference points for understanding the developmental trajectory of generative models and guiding their future evolution.