零样本机器遗忘新方法，防止模型过拟合退化。
Zero-Shot Machine Unlearning with Proxy Adversarial Data Generation
- 用对抗样本模拟缺失数据，逼近真实剩余数据分布
- 在特定子空间中执行遗忘，避免性能显著下降
- 基于影响的伪标签策略，提升遗忘后模型表现
机器遗忘旨在消除训练模型中特定样本的影响。现有方法依赖剩余数据以防止过遗忘（即模型在其余数据上性能显著下降），但在仅能获取待遗忘样本的零样本场景下难以应用。本文提出ZS-PAG框架，通过生成对抗性样本逼近不可访问的剩余数据；利用生成样本定位特定子空间进行遗忘操作，从而在零样本场景下避免过遗忘；同时考虑遗忘过程对剩余样本的影响，设计基于影响的伪标签策略。实验表明，该方法在多个基准测试中优于现有基线，并具备理论保证。
原文摘要 · Abstract (English)
Machine unlearning aims to remove the influence of specific samples from a trained model. A key challenge in this process is over-unlearning, where the model's performance on the remaining data significantly drops due to the change in the model's parameters. Existing unlearning algorithms depend on the remaining data to prevent this issue. As such, these methods are inapplicable in a more practical scenario, where only the unlearning samples are available (i.e., zero-shot unlearning). This paper presents a novel framework, ZS-PAG, to fill this gap. Our approach offers three key innovations: (1) we approximate the inaccessible remaining data by generating adversarial samples; (2) leveraging the generated samples, we pinpoint a specific subspace to perform the unlearning process, therefore preventing over-unlearning in the challenging zero-shot scenario; and (3) we consider the influence of the unlearning process on the remaining samples and design an influence-based pseudo-labeling strategy. As a result, our method further improves the model's performance after unlearning. The proposed method holds a theoretical guarantee, and experiments on various benchmarks validate the effectiveness and superiority of our proposed method over several baselines.
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