Summary of Diminishing Stereotype Bias in Image Generation Model Using Reinforcemenlent Learning Feedback, by Xin Chen et al.
Diminishing Stereotype Bias in Image Generation Model using Reinforcemenlent Learning Feedback
by Xin Chen, Virgile Foussereau
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This study tackles gender bias in image generation models using a novel Denoising Diffusion Policy Optimization (DDPO) pipeline with Reinforcement Learning from Artificial Intelligence Feedback (RLAIF). The approach employs a pretrained stable diffusion model, a Transformer-based gender classification module, and two reward functions: Rshift for shifting gender imbalances and Rbalance for maintaining balance. Experiments show the effectiveness of this method in reducing bias without compromising image quality or requiring additional data or prompts. This research establishes a foundation for addressing various forms of bias in AI systems, highlighting the need for responsible AI development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to make image generation models fairer by reducing gender bias. It uses a special kind of AI feedback and a process called Denoising Diffusion Policy Optimization (DDPO) to create new images that are more balanced between men and women. The results show that this approach can reduce bias without making the images look worse or requiring extra data. This study shows how important it is for AI developers to be responsible and make sure their models don’t have biases. |
Keywords
» Artificial intelligence » Classification » Diffusion » Diffusion model » Image generation » Optimization » Reinforcement learning » Transformer
