Forgetting Similar Samples: Can Machine Unlearning Do it Better?

Summary

This paper examines whether existing machine unlearning methods truly remove the influence of target training samples when the training dataset contains many similar samples. Experiments on four constructed datasets for image and language models show a notable gap between expected and actual performance for most existing methods, including the retraining-from-scratch baseline. The authors also explore potential solutions to improve current unlearning approaches.

Topics

• Training data and privacy