Machine unlearning aims to enable models to forget specific data instances
when receiving deletion requests. Current research centres on efficient
unlearning to erase the influence of data from the model and neglects the
subsequent impacts on the remaining data. Consequently, existing unlearning
algorithms degrade the model's performance after unlearning, known as
\textit{over-unlearning}. This paper addresses this critical yet under-explored
issue by introducing machine \underline{U}nlearning via \underline{N}ull
\underline{S}pace \underline{C}alibration (UNSC), which can accurately unlearn
target samples without over-unlearning. On the contrary, by calibrating the
decision space during unlearning, UNSC can significantly improve the model's
performance on the remaining samples. In particular, our approach hinges on
confining the unlearning process to a specified null space tailored to the
remaining samples, which is augmented by strategically pseudo-labeling the
unlearning samples. Comparative analyses against several established baselines
affirm the superiority of our approach. Code is released at this
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通过介绍机器学习中的 Null Space Calibration (UNSC)，本文解决了机器遗忘中的过度遗忘问题，并通过在遗忘过程中校准决策空间，显著改善了模型在剩余样本上的性能。