通过解耦特征与扰动损失，实现模型高效删数且保持性能稳定。
Factor Decorrelation Enhanced Data Removal from Deep Predictive Models
- 引入动态权重调整的特征解耦模块，降低特征冗余与相关性。
- 采用带损失扰动的平滑删数机制，防止数据泄露。
- 在5个数据集上表现优异，尤其在分布外场景更稳健。
用户隐私保护与合规需求推动了模型训练中敏感数据删除的必要性，但该过程常引发分布偏移，导致模型性能下降，尤其在分布外（OOD）场景下更为明显。本文提出一种新型数据删除方法，通过因子解耦与损失扰动增强深度预测模型。方法包含：(1) 采用动态自适应权重调整和迭代表示更新的判别性保持型因子解耦模块，减少特征冗余并最小化特征间相关性；(2) 带有损失扰动的平滑数据删除机制，构建信息论层面的数据泄露防护。在五个基准数据集上的大量实验表明，该方法优于现有基线，在显著分布偏移下仍能保持高预测精度与鲁棒性。结果凸显其在分布内与分布外场景下的高效性与适应性。
原文摘要 · Abstract (English)
The imperative of user privacy protection and regulatory compliance necessitates sensitive data removal in model training, yet this process often induces distributional shifts that undermine model performance-particularly in out-of-distribution (OOD) scenarios. We propose a novel data removal approach that enhances deep predictive models through factor decorrelation and loss perturbation. Our approach introduces: (1) a discriminative-preserving factor decorrelation module employing dynamic adaptive weight adjustment and iterative representation updating to reduce feature redundancy and minimize inter-feature correlations. (2) a smoothed data removal mechanism with loss perturbation that creates information-theoretic safeguards against data leakage during removal operations. Extensive experiments on five benchmark datasets show that our approach outperforms other baselines and consistently achieves high predictive accuracy and robustness even under significant distribution shifts. The results highlight its superior efficiency and adaptability in both in-distribution and out-of-distribution scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。