Summary of Fairrag: Fair Human Generation Via Fair Retrieval Augmentation, by Robik Shrestha et al.
FairRAG: Fair Human Generation via Fair Retrieval Augmentation
by Robik Shrestha, Yang Zou, Qiuyu Chen, Zhiheng Li, Yusheng Xie, Siqi Deng
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 Existing text-to-image generative models perpetuate societal biases embedded in their training data, particularly for human image generation where certain demographic groups are underrepresented. This paper proposes Fair Retrieval Augmented Generation (FairRAG), a novel framework that conditions pre-trained generative models on reference images from an external database to improve fairness. FairRAG utilizes a lightweight linear module to project reference images into the textual space, enabling conditioning. To enhance fairness, FairRAG incorporates simple debiasing strategies, providing diverse demographic groups during the generative process. The paper demonstrates that FairRAG outperforms existing methods in terms of demographic diversity, image-text alignment, and image fidelity while maintaining minimal computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if computers could create pictures based on words. Right now, these models often reflect and even make worse the biases we see in our society. This is especially true for creating images of people from different backgrounds. The current solutions to fix this problem aren’t very effective because they rely on flawed starting points. In this research, scientists introduce a new approach called FairRAG that helps computers create more diverse and fair images by looking at examples from other databases. They also use simple tricks to make sure the generated images are not biased against certain groups of people. The results show that their method is better than previous ones in terms of fairness, image quality, and alignment with text descriptions. |
Keywords
* Artificial intelligence * Alignment * Image generation * Retrieval augmented generation
