  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. We provide theoretical
analyses demonstrating why properly introducing label smoothing improves MU
performance. We conducted extensive experiments on six datasets of various
sizes and different modalities, demonstrating the effectiveness and robustness
of our proposed method. The consistent improvement in MU performance is only at
a marginal cost of additional computations. For instance, UGradSL improves over
the gradient ascent MU baseline by 66% unlearning accuracy without sacrificing
unlearning efficiency.
