  Diffusion Models are popular generative modeling methods in various vision
tasks, attracting significant attention. They can be considered a unique
instance of self-supervised learning methods due to their independence from
label annotation. This survey explores the interplay between diffusion models
and representation learning. It provides an overview of diffusion models'
essential aspects, including mathematical foundations, popular denoising
network architectures, and guidance methods. Various approaches related to
diffusion models and representation learning are detailed. These include
frameworks that leverage representations learned from pre-trained diffusion
models for subsequent recognition tasks and methods that utilize advancements
in representation and self-supervised learning to enhance diffusion models.
This survey aims to offer a comprehensive overview of the taxonomy between
diffusion models and representation learning, identifying key areas of existing
concerns and potential exploration. Github link:
https://github.com/dongzhuoyao/Diffusion-Representation-Learning-Survey-Taxonomy
