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About this paper
Md Manjurul Ahsan, Shivakumar Raman, Yingtao Liu, Zahed Siddique
This survey provides a comprehensive overview of Diffusion Models, consolidating the fragmented literature from 2020 to 2024 by systematically reviewing 85 papers. It categorizes DM principles, application areas, and innovations across diverse fields including media synthesis, healthcare, and materials science, while also outlining current limitations and future research directions.
Key insights
- Diffusion Models (DMs) demonstrate remarkable versatility, extending beyond traditional computer vision to applications in healthcare, materials science, cybersecurity, and atmospheric science.
- Despite their capabilities, DMs face challenges including high computational intensity, generalizability issues, and trade-offs between generation quality and control.
- Innovative techniques are emerging to address DM limitations, focusing on efficiency, enhanced control, and robustness against adversarial attacks.
Summary by alphaXiv.
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