Continual Data Unlearning in Diffusion Models via Transition-based Regularization
A continual data unlearning framework that uses completed deletion transitions as directional references to regularize future updates and achieves a better balance between deletion persistence and generative utility than existing unlearning baselines as requests accumulate.
Sunbeom Jeong, Sehwan Kim, Sang-Woo Hong et al.
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