Summary of Embedding An Ethical Mind: Aligning Text-to-image Synthesis Via Lightweight Value Optimization, by Xingqi Wang et al.
Embedding an Ethical Mind: Aligning Text-to-Image Synthesis via Lightweight Value Optimization
by Xingqi Wang, Xiaoyuan Yi, Xing Xie, Jia Jia
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 Recent advancements in diffusion models have enabled the generation of realistic images, but often produce harmful content misaligned with human values. Despite research on Large Language Models (LLMs), the Text-to-Image (T2I) model alignment challenge remains unexplored. The proposed LiVO method optimizes a lightweight value encoder to integrate a specified value principle with the input prompt, controlling generated images’ semantics and values. A diffusion model-tailored preference optimization loss is designed, theoretically approximating the Bradley-Terry model used in LLM alignment but providing a more flexible trade-off between image quality and value conformity. To optimize the value encoder, an automatic text-image preference dataset of 86k samples is constructed. Without updating most model parameters and through adaptive value selection from the input prompt, LiVO reduces harmful outputs and achieves faster convergence, surpassing several strong baselines and taking a step towards ethically aligned T2I models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with computers that can create very realistic images. Right now, these computers often make bad things like biased or offensive content. The researchers wanted to fix this by making the computer generate better images that match human values. They created a new method called LiVO that helps the computer understand what kind of image it should create based on certain rules. This way, the computer can make more good images and fewer bad ones. They tested their method with many examples and found it worked well. The goal is to make computers that can generate images in a way that’s fair and respectful. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Diffusion model » Encoder » Optimization » Prompt » Semantics
