AI RESEARCH
GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
arXiv CS.AI
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Training-data attribution for vision generative models aims to identify which training data influenced a given output. While most methods score individual examples, practitioners often need group-level answers (e.g., artistic styles or object classes). A natural realization of this counterfactual is Leave-One-Group-Out (LOGO) retraining, which retrains the mod