RAIGen: Rare Attribute Identification in Text-to-Image Generative Models 事件

RAIGen: Rare Attribute Identification in Text-to-Image Generative Models arXiv:2602.06806v2 Announce Type: replace Abstract: Text-to-image diffusion models achieve impressive generation quality but inherit and amplify training-data biases, skewing coverage of semantic attributes. Prior work addresses this in two ways. Closed-set approaches mitigate biases in predefined fairness categories (e.g., gender, race), assuming socially salient minority attributes are known a priori. Open-set approaches