Personalize at Test Time: Learning User Preferences for Image Generation
We propose an approach that learns personalized reward models directly from users' historical image preference pairs.
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Key points
- Diffusion models can generate high-quality images, yet aligning their outputs with individual user preferences remains challenging.
- A key bottleneck is accurately modeling diverse user preferences from limited feedback.
- Existing approaches often rely on labor-intensive manual preference annotations or vision-language models (VLM) to extract preference information from user interaction histories, introducing substantial annotation or computational costs that limit scalability.
- Experiments on real-user preference data show that our approach achieves approximately 77% held-out pairwise preference prediction accuracy and improves the alignment of generated images with individual user preferences.
Sources (1)
- [1]Personalize at Test Time: Learning User Preferences for Image GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 07:10 PM
We propose an approach that learns personalized reward models directly from users' historical image preference pairs.
Diffusion models can generate high-quality images, yet aligning their outputs with individual user preferences remains challenging.
Extractive summary: sentences quoted from the sources.
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