提出轻量级模型遗忘方法，用少量数据快速删除特定类别且性能损失小。
DFMU: Data-Frugal Machine Unlearning
- 仅需一次前向与反向传播计算模块重要性分数。
- 用13%数据实现比顶尖方法高40%的保留准确率。
- 适合资源受限场景下高效移除模型中特定类别的需求。
机器遗忘是一个新兴领域，旨在安全地从训练好的模型中移除特定元素（包括概念、属性、实体和类别），同时尽可能减少模型性能下降。现有方法通常依赖重新训练来实现遗忘，计算成本高。本文提出数据轻量型机器遗忘（DFMU）方法，仅需一次前向和反向传播即可计算模型各计算模块的重要性分数，基于知识保持的剪枝策略，能更快收敛且所需数据远少于现有方法。实验表明，在多个公开数据集上，该方法仅使用13%的数据样本，就实现了比当前最优方法高出40%的保留准确率，且遗忘单个类别的平均处理时间快88%。
原文摘要 · Abstract (English)
Machine unlearning is an emerging domain that ensures the safe removal of elements (includes concepts, attributes, entity and class) from the trained model along with least drop in model performance. The domain of machine unlearning brings its own indigenous challenges since the removal of pre-trained elements from model will always degrade the model performance on remaining elements. The existing methods basically rely on retraining for removal of elements from the pre-trained model, which is compute extensive. In this work, we propose a machine unlearning method which helps to reduce the computational requirement for faster retain-dataset accuracy convergence which also does not require extensive retraining of the pre-trained model. The proposed method, Data-Frugal Machine Unlearning (DFMU) requires only a single forward and backward pass for computing the importance score of various computational blocks of a model. The importance score computation is based on knowledge preserving pruning which helps to converge faster and requires far less data as compared to the existing methods. Experimentally, it achieves 40% more retain-accuracy with just 13% of data samples in comparison with SOTA method on various public datasets and also averages 88% faster processing time for forgetting a given class.
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