Unlearning with Fisher Masking
Yufang Liu Changzhi Sun Yuanbin Wu Aimin Zhou
Machine unlearning aims to revoke some training data after learning in
response to requests from users, model developers, and administrators. Most
previous methods are based on direct fine-tuning, which may neither remove data
completely nor retain full performances on the remain data. In this work, we
find that, by first masking some important parameters before fine-tuning, the
performances of unlearning could be significantly improved. We propose a new
masking strategy tailored to unlearning based on Fisher information.
Experiments on various datasets and network structures show the effectiveness
of the method: without any fine-tuning, the proposed Fisher masking could
unlearn almost completely while maintaining most of the performance on the
remain data. It also exhibits stronger stability compared to other unlearning
baselines