提出孪生机制，让模型遗忘数据时更贴近真实分类表现。
Towards Aligned Data Forgetting via Twin Machine Unlearning
- 设计孪生学习任务，用辅助模型预测遗忘后应有准确率
- 实验证明遗忘模型与原始模型的分类准确率对齐度显著提升
- 适合关注隐私保护中模型行为一致性的研究者
现代隐私法规推动了机器遗忘技术的发展，使训练好的模型能够高效地遗忘特定训练数据。以往的遗忘方法常将‘数据遗忘’定义为在该数据上实现零分类准确率，但真正的目标应是让遗忘后的模型与原始黄金模型保持对齐，即两者分类准确率一致。然而，由于原始模型具备泛化能力，其准确率通常非零。为此，我们提出孪生机器遗忘（Twin Machine Unlearning, TMU），为原遗忘问题构建一个对应的孪生问题。通过在孪生问题上训练的泛化-标签预测器，可迁移至原问题，实现对齐的数据遗忘。大量实验证明，该方法显著提升了遗忘模型与黄金模型之间的对齐程度。
原文摘要 · Abstract (English)
Modern privacy regulations have spurred the evolution of machine unlearning, a technique enabling a trained model to efficiently forget specific training data. In prior unlearning methods, the concept of "data forgetting" is often interpreted and implemented as achieving zero classification accuracy on such data. Nevertheless, the authentic aim of machine unlearning is to achieve alignment between the unlearned model and the gold model, i.e., encouraging them to have identical classification accuracy. On the other hand, the gold model often exhibits non-zero classification accuracy due to its generalization ability. To achieve aligned data forgetting, we propose a Twin Machine Unlearning (TMU) approach, where a twin unlearning problem is defined corresponding to the original unlearning problem. Consequently, the generalization-label predictor trained on the twin problem can be transferred to the original problem, facilitating aligned data forgetting. Comprehensive empirical experiments illustrate that our approach significantly enhances the alignment between the unlearned model and the gold model.
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