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arXiv AI By Joan Serr\`a, Dipam Goswami, Fabio Morreale, Wei-Hsiang Liao, Yuki Mitsufuji

Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew

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The paper introduces MUCS, a method for training data attribution (TDA) in diffusion models that uses mirrored unlearning and noise-consistent skew. By fine-tuning a second model with bounded mirrored gradient ascent and measuring its normalized skew relative to the original model using consistent noise samples, MUCS achieves significantly better performance than existing TDA methods across three datasets. The authors also explore design choices, overlap of influential instances, and the potential of ensembling TDA approaches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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