🤖 AI Summary
Despite their remarkable performance, diffusion models lack a systematic survey and a unified taxonomic framework. Method: This paper introduces the first comprehensive taxonomy encompassing methodological evolution and cross-domain applications, systematically reviewing over 300 seminal works published between 2015 and 2024. It focuses on three core research directions: efficient sampling, improved likelihood estimation, and modeling of structured data—covering key techniques including denoising score matching, stochastic differential equation (SDE) solvers, latent-space distillation, conditional guidance, and cross-modal joint modeling. Contribution/Results: We propose a novel integration paradigm that synergizes diffusion models with other generative paradigms. Furthermore, we publicly release a structured literature repository and a dynamically updated classification system, which has become a standard reference resource in the field.
📝 Abstract
Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language processing, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github: https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy
