PECKER uses a saliency mask to prioritize parameter updates in distillation-based unlearning, achieving shorter training times for class and concept forgetting on CIFAR-10 and STL-10 while matching prior methods' efficacy.
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PECKER: A Precisely Efficient Critical Knowledge Erasure Recipe For Machine Unlearning in Diffusion Models
PECKER uses a saliency mask to prioritize parameter updates in distillation-based unlearning, achieving shorter training times for class and concept forgetting on CIFAR-10 and STL-10 while matching prior methods' efficacy.