# Diffusion Models: A Comprehensive Survey of Methods and Applications

**Authors:** Ling Yang, Zhilong Zhang, Shenda Hong  
**arXiv:** [2209.00796](https://arxiv.org/abs/2209.00796)  
**Date:** 2022-09-02  
**Summary page:** https://www.summarizepaper.com/en/paper/2209.00796v1/

## Key points

- Diffusion models are a class of deep generative models known for impressive results and strong theoretical foundation
- Challenges faced by diffusion models include costly sampling procedures and sub-optimal likelihood estimation
- Recent research efforts focus on enhancing the performance of diffusion models
- Authors Ling Yang, Zhilong Zhang, and Shenda Hong categorize diffusion model variants into three types: sampling-acceleration enhancement, likelihood-maximization enhancement, and data-generalization enhancement
- The review discusses connections between diffusion models and other generative models like variational autoencoders, generative adversarial networks, normalizing flow, autoregressive models, and energy-based models
- Applications of diffusion models span various domains such as computer vision, natural language processing, waveform signal processing, multi-modal modeling, molecular graph generation, time series modeling, and adversarial purification
- The review offers new perspectives on the development of diffusion models by identifying areas for improvement and suggesting future research directions

## Summary

Diffusion models are a class of deep generative models that have garnered attention for their impressive results on various tasks, supported by a strong theoretical foundation. While these models have demonstrated superior quality and diversity in sample synthesis compared to other state-of-the-art models, they still face challenges such as costly sampling procedures and sub-optimal likelihood estimation. Recent research efforts have been focused on enhancing the performance of diffusion models. In this comprehensive review titled "Diffusion Models: A Comprehensive Survey of Methods and Applications," authors Ling Yang, Zhilong Zhang, and Shenda Hong delve into the existing variants of diffusion models. They introduce a taxonomy that categorizes these variants into three types: sampling-acceleration enhancement, likelihood-maximization enhancement, and data-generalization enhancement. Additionally, the authors provide detailed insights into five other generative models - variational autoencoders, generative adversarial networks, normalizing flow, autoregressive models, and energy-based models - elucidating the connections between diffusion models and these approaches. The review further explores the applications of diffusion models across various domains including computer vision, natural language processing, waveform signal processing, multi-modal modeling, molecular graph generation, time series modeling, and adversarial purification. By examining these diverse applications,the authors highlight the versatility and potential impact of diffusion models in advancing different fields. Moreover,the review offers new perspectives on the development of diffusion models.By identifying areas for improvementand suggesting future directions for researchand application development in this space,the authors contribute valuable insights to the ongoing evolution of generative modeling techniques. Overall,this comprehensive survey serves as a valuable resource for researchersand practitioners seeking to deepen their understandingof diffusionmodelsand explore their potentialin solving complex real-world problems across various domains.