Apple、不要データ削除コストを削減する新手法発表

While state-of-the-art unlearning methods have emerged in response, they typically treat all points in the forget set equally.

In this work, we challenge this approach by asking: do points that have a negligible impact on the model's learning need to be removed?

Through a comparative analysis of influence functions across language and vision tasks, we identify subsets of training data with negligible impact on model outputs