Summary of Versusdebias: Universal Zero-shot Debiasing For Text-to-image Models Via Slm-based Prompt Engineering and Generative Adversary, by Hanjun Luo et al.
VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary
by Hanjun Luo, Ziye Deng, Haoyu Huang, Xuecheng Liu, Ruizhe Chen, Zuozhu Liu
First submitted to arxiv on: 28 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 The proposed VersusDebias framework is a novel, universal debiasing approach for Text-to-Image (T2I) models, tackling biases against demographic social groups. The framework consists of an array generation module and an image generation module, enabling zero-shot debiasing of arbitrary T2I models across multiple attributes simultaneously. Extensive experiments demonstrate VersusDebias’s effectiveness in debiasing gender, race, and age biases, outperforming existing methods in both zero-shot and few-shot scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VersusDebias is a new way to make text-to-image models fairer. Right now, these models often create biased images that favor certain groups over others. The problem is that the current solutions are limited to specific models and prompts, which isn’t very practical. They also don’t account for when the model makes mistakes or creates things that aren’t supposed to be there. VersusDebias fixes all of these issues by creating a framework that can work with any text-to-image model, regardless of what it’s trying to create. |
Keywords
» Artificial intelligence » Few shot » Image generation » Zero shot
