Summary of Trustworthy Text-to-image Diffusion Models: a Timely and Focused Survey, by Yi Zhang et al.
Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey
by Yi Zhang, Zhen Chen, Chih-Hong Cheng, Wenjie Ruan, Xiaowei Huang, Dezong Zhao, David Flynn, Siddartha Khastgir, Xingyu Zhao
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper presents a comprehensive survey on trustworthy Text-to-Image (T2I) Diffusion Models (DMs). The authors highlight the growing importance of evaluating non-functional properties, such as robustness, fairness, security, privacy, factuality, and explainability, in T2I DMs. They summarize recent methods for investigating trustworthiness in T2I DMs via falsification, enhancement, verification & validation, and assessment. The authors also review benchmarks and domain applications of T2I DMs, identifying gaps in current research and proposing future directions to advance the development of trustworthy T2I DMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a special type of computer program that can create images from text. These programs are very good at making realistic pictures, but people are worried about some things they do. The authors want to find out what makes these programs work well or not so well. They looked at what other experts have said on this topic and found some problems with the ways they do it. Now, they’re trying to help solve those problems by giving a big picture of how to make better image-making programs. |
Keywords
» Artificial intelligence » Diffusion
