Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints 事件

Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints arXiv:2605.30825v1 Announce Type: cross Abstract: Unlearning in diffusion models aims to remove undesirable data or concepts while preserving the utility of pretrained models -- two fundamentally conflicting objectives. We propose a principled constrained optimization framework that formulates unlearning as minimizing the deviation from a pretrained model, subject to explicit separation constraints