Summary of On the Fairness, Diversity and Reliability Of Text-to-image Generative Models, by Jordan Vice et al.
On the Fairness, Diversity and Reliability of Text-to-Image Generative Models
by Jordan Vice, Naveed Akhtar, Richard Hartley, Ajmal Mian
First submitted to arxiv on: 21 Nov 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 The multimodal generative model has sparked discussions on its fairness, reliability, and potential misuse. While text-to-image models can produce high-fidelity images, they also exhibit unpredictable behavior and vulnerabilities that can be exploited to manipulate class or concept representations. A new evaluation framework is proposed to assess model reliability through responses to semantic perturbations in the embedding space, pinpointing inputs that trigger unreliable behavior. The approach evaluates generative diversity and fairness, examining how removing concepts from input prompts affects semantic guidance. This method lays the groundwork for detecting unreliable models and retrieving bias provenance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models can make beautiful images, but they also have some problems. They can be tricked into making biased or fake pictures. To solve this issue, researchers developed a way to test these models by changing their “brain” or “memory”. This helps us understand how good the model is at making new and diverse pictures, and if it’s fair in what it creates. The goal is to make sure these models don’t create unfair or fake things. |
Keywords
» Artificial intelligence » Embedding space » Generative model
