arXiv cs.AI
· Papers
Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearning
arXiv:2510.17917v2 Announce Type: replace-cross Abstract: Data unlearning aims to remove the influence of specific training samples from a trained model. In fine-tuning methods, data unlearning relies primarily on loss maximization over forget samples, which often leads to quality degradation or incomplete forgetting.