Summary of Advances in Diffusion Models For Image Data Augmentation: a Review Of Methods, Models, Evaluation Metrics and Future Research Directions, by Panagiotis Alimisis et al.
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions
by Panagiotis Alimisis, Ioannis Mademlis, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis, Georgios Th. Papadopoulos
First submitted to arxiv on: 4 Jul 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 In this paper, researchers explore the use of Diffusion Models (DMs) for image data augmentation, a crucial methodology in modern computer vision tasks. DMs have emerged as powerful tools for generating realistic and diverse images by learning the underlying data distribution. The study provides a comprehensive review of DM-based approaches for image augmentation, covering strategies, tasks, and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a superpower that lets you change any picture to make it look however you want! This is what Diffusion Models can do. They’re like magic editors that can make pictures more realistic or diverse. In this study, scientists looked at how these models work and used them to improve image editing. They also talked about the different ways to use DMs for editing and showed examples of what they can do. |
Keywords
» Artificial intelligence » Data augmentation » Diffusion
