数据增强能显著提升模型删忘效果，降低隐私泄露风险。
Data Augmentation Improves Machine Unlearning
- 设计合理的数据增强策略可提升删忘方法性能。
- 使用TrivialAug增强后，平均差距指标降低40.12%。
- 适合关注模型隐私保护与高效删忘的研究者。
机器删忘（MU）旨在移除特定数据对已训练模型的影响，同时保持模型在剩余数据上的性能。尽管已有研究指出记忆性与数据增强之间的关联，但系统性增强设计在删忘中的作用仍缺乏深入探讨。本文研究了不同数据增强策略对删忘方法（包括SalUn、随机标签和微调）的影响。在CIFAR-10和CIFAR-100上，于不同遗忘率条件下进行实验，结果表明合理设计的增强策略能显著提升删忘有效性，缩小与重新训练模型之间的性能差距。使用TrivialAug增强时，平均差距指标最多降低40.12%。结果表明，数据增强不仅能减少记忆现象，还在实现隐私保护与高效删忘中起关键作用。
原文摘要 · Abstract (English)
Machine Unlearning (MU) aims to remove the influence of specific data from a trained model while preserving its performance on the remaining data. Although a few works suggest connections between memorisation and augmentation, the role of systematic augmentation design in MU remains under-investigated. In this work, we investigate the impact of different data augmentation strategies on the performance of unlearning methods, including SalUn, Random Label, and Fine-Tuning. Experiments conducted on CIFAR-10 and CIFAR-100, under varying forget rates, show that proper augmentation design can significantly improve unlearning effectiveness, reducing the performance gap to retrained models. Results showed a reduction of up to 40.12% of the Average Gap unlearning Metric, when using TrivialAug augmentation. Our results suggest that augmentation not only helps reduce memorization but also plays a crucial role in achieving privacy-preserving and efficient unlearning.
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