29 мар. 2024 г. · A novel framework that conditions pre-trained generative models on reference images retrieved from an external image database to improve fairness in human ...

In this work, we introduce Fair Retrieval Augmented Generation. (FairRAG), a novel framework that conditions pre-trained generative models on reference images ...

We propose the FairRAG framework to improve fairness in human image generation by using demographically diverse reference images. To achieve this, FairRAG ...

A novel framework that conditions pre-trained generative models on reference images retrieved from an external image database to improve fairness in human ...

23 сент. 2024 г. · Download Citation | On Jun 16, 2024, Robik Shrestha and others published FairRAG: Fair Human Generation via Fair Retrieval Augmentation ...

8 апр. 2024 г. · The paper introduces FairRAG, a method that leverages external references containing diverse samples to improve fairness in text-to-image ...

30 мая 2024 г. · By conditioning the generative process on demographically diverse reference images, FairRAG provides a convenient way to ensure fairness without ...

21 сент. 2024 г. · Our findings indicate that RAG systems with fair rankings can maintain a high level of generation quality and, in many cases, even outperform ...

FairRAG: Fair Human Generation via Fair Retrieval Augmentation. R Shrestha, Y Zou, Q Chen, Z Li, Y Xie, S Deng. Proceedings of the IEEE/CVF Conference on ...

We propose FairRAG to mitigate demographic biases of diffusion models (e.g., Stable Diffusion) based on Retrieval Augmented Generation (RAG). clean-usnob ...