Diffusion models have shown high-quality sample generation ability in the field of generative models,and constantly set new records for image generation evaluation indicator FID scores since their introduction,and has become a research hotspot in this field.However,related reviews of this kind are scarce in China.Therefore,this paper aims to summarize and analyze the re-search on related diffusion generative models.Firstly,it analyzes the related derivative models in each basic diffusion model,which focus on optimizing internal algorithms and efficient sampling,by discussing the characteristics and principles of three common models:denoising diffusion probabilistic model,score-based diffusion generative model,and diffusion generative model based on random differential equations.Secondly,it summarizes the current applications of diffusion models in computer vision,natural lan-guage processing,time series,multimodal,and interdisciplinary fields.Finally,based on the above discussion,relevant suggestions for the existing limitations of diffusion generative models are proposed,such as long sampling times and multiple sampling steps,and a research direction for the future development of diffusion generative models is provided based on previous studies.
NETL
NSTL
万方数据
