Personalize at Test Time: Learning User Preferences for Image Generation
This formulation reduces per-user adaptation to optimizing a low-dimensional weight vector, simplifying optimization and enabling data-efficient personalization from sparse feedback and improves the alignment of generated images with individual user preferences.
Jiamu Bai, Jia-Ming Hu, Yan-Hong Wu et al.
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