Label Smoothing Improves Machine Unlearning

Understand

The objective of machine unlearning (MU) is to eliminate previously learned data from a model.

• However, it is challenging to strike a balance between computation cost and performance when using existing MU techniques.
• Taking inspiration from the influence of label smoothing on model confidence and differential privacy, we propose a simple gradient-based MU approach that uses an inverse process of label smoothing.
• This work introduces UGradSL, a simple, plug-and-play MU approach that uses smoothed labels.