To comply with AI and data regulations, the need to forget private or
copyrighted information from trained machine learning models is increasingly
important. The key challenge in unlearning is forgetting the necessary data in
a timely manner, while preserving model performance. In this work, we address
the zero-shot unlearning scenario, whereby an unlearning algorithm must be able
to remove data given only a trained model and the data to be forgotten. Under
such a definition, existing state-of-the-art methods are insufficient. Building
on the concepts of Lipschitz continuity, we present a method that induces
smoothing of the forget sample's output, with respect to perturbations of that
sample. We show this smoothing successfully results in forgetting while
preserving general model performance. We perform extensive empirical evaluation
of our method over a range of contemporary benchmarks, verifying that our
method achieves state-of-the-art performance under the strict constraints of
zero-shot unlearning.

在 AI 和数据法规的合规要求下，从训练的机器学习模型中忘记私人或受版权保护的信息变得越来越重要。在本研究中，我们解决了零样本遗忘的挑战，即一个遗忘算法必须能够在只有训练好的模型和需要被遗忘的数据的情况下移除数据。基于 Lipschitz 连续性的概念，我们提出了一种方法来引导遗忘样本输出的平滑化，以应对该样本的扰动。我们展示了这种平滑化成功地实现了遗忘同时保持模型性能的目标。我们在多个现代基准测试中对我们的方法进行了广泛的经验评估，验证了我们的方法在严格的零样本遗忘约束下达到了最先进的性能。

通过李普希茨正则化实现规模化的零样本机器遗忘

Zero-Shot Machine Unlearning at Scale via Lipschitz Regularization

Machine \emph{unlearning}, which involves erasing knowledge about a
\emph{forget set} from a trained model, can prove to be costly and infeasible
by existing techniques. We propose a nearly compute-free zero-shot unlearning
technique based on a discrete representational bottleneck. We show that the
proposed technique efficiently unlearns the forget set and incurs negligible
damage to the model's performance on the rest of the data set. We evaluate the
proposed technique on the problem of \textit{class unlearning} using three
datasets: CIFAR-10, CIFAR-100, and LACUNA-100. We compare the proposed
technique to SCRUB, a state-of-the-art approach which uses knowledge
distillation for unlearning. Across all three datasets, the proposed technique
performs as well as, if not better than SCRUB while incurring almost no
computational cost.

基于离散表示瓶颈的几乎无计算成本的零样本遗忘技术能有效遗忘忘记集，对数据集余下的部分几乎没有性能损失，并在 CIFAR-10、CIFAR-100 和 LACUNA-100 的三个数据集上进行了评估，与知识蒸馏用于遗忘的先进方法 SCRUB 相比，这种技术表现出与 SCRUB 几乎无计算成本相当甚至更好的性能。