基于随机教师网络的机器遗忘方法
Machine Unlearning Methodology base on Stochastic Teacher Network
AI总结：
针对现有机器遗忘方法无法快速移除深度学习模型知识的问题，本文提出基于随机教师网络的方法，可在单个epoch内高效消除目标数据影响，重建模型性能与重训练模型相当。
摘要：
“被遗忘权”现象的兴起推动了机器遗忘（machine unlearning）领域的研究，该技术赋予数据所有者主动撤回已用于模型训练的数据的权利，同时要求消除这些数据对模型的贡献。实现这一目标的一种简单方法是使用剩余数据重新训练模型，但这对于继续参与训练的其他数据所有者而言是不可接受的。现有机器遗忘方法被发现无法有效快速地从深度学习模型中移除知识。本文提出使用一个随机网络作为教师网络，加快消除被遗忘数据对模型造成的影响。我们在三个数据集上开展了实验，结果表明，所提方法能在单个epoch内有效消除目标数据对模型的影响，可实现模型的一次性擦除与重建，且重建模型达到了与重训练模型相同的性能。
英文摘要：
The rise of the phenomenon of the "right to be forgotten" has prompted research on machine unlearning, which grants data owners the right to actively withdraw data that has been used for model training, and requires the elimination of the contribution of that data to the model. A simple method to achieve this is to use the remaining data to retrain the model, but this is not acceptable for other data owners who continue to participate in training. Existing machine unlearning methods have been found to be ineffective in quickly removing knowledge from deep learning models. This paper proposes using a stochastic network as a teacher to expedite the mitigation of the influence caused by forgotten data on the model. We performed experiments on three datasets, and the findings demonstrate that our approach can efficiently mitigate the influence of target data on the model within a single epoch. This allows for one-time erasure and reconstruction of the model, and the reconstruction model achieves the same performance as the retrained model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
