Summary of Diffusion Models and Representation Learning: a Survey, by Michael Fuest et al.
Diffusion Models and Representation Learning: A Survey
by Michael Fuest, Pingchuan Ma, Ming Gui, Johannes Schusterbauer, Vincent Tao Hu, Bjorn Ommer
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey explores the connection between diffusion models and representation learning, which enables self-supervised learning without labeled data. The paper discusses the mathematical foundations of diffusion models, their denoising network architectures, and guidance methods. It also examines various approaches that utilize representations learned from pre-trained diffusion models for recognition tasks or enhance diffusion models using advancements in representation and self-supervised learning. This comprehensive overview aims to identify key areas of concern and potential exploration between diffusion models and representation learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how a type of artificial intelligence called diffusion models can learn from data without being told what’s correct or incorrect. It talks about the basics of these models, how they work, and different ways people use them to improve their performance. The goal is to understand the connections between these models and another important area of AI called representation learning. |
Keywords
» Artificial intelligence » Diffusion » Representation learning » Self supervised
