Machine Unlearning by Suppressing Sample Contribution
Xinwen Cheng Zhehao Huang Xiaolin Huang
Machine Unlearning (MU) is to forget data from a well-trained model, which is
practically important due to the "right to be forgotten". In this paper, we
start from the fundamental distinction between training data and unseen data on
their contribution to the model: the training data contributes to the final
model while the unseen data does not. We theoretically discover that the input
sensitivity can approximately measure the contribution and practically design
an algorithm, called MU-Mis (machine unlearning via minimizing input
sensitivity), to suppress the contribution of the forgetting data. Experimental
results demonstrate that MU-Mis outperforms state-of-the-art MU methods
significantly. Additionally, MU-Mis aligns more closely with the application of
MU as it does not require the use of remaining data.