Recent regulation on right-to-be-forgotten emerges tons of interest in unlearning pre-trained machine learning models. While approximating a straightforward yet expensive approach of retrain-from-scratch, recent machine unlearning methods unlearn a sample by updating weights to remove its influence on the weight parameters. In this paper, we introduce a simple yet effective approach to remove a data influence on the deep generative model. Inspired by works in multi-task learning, we propose to manipulate gradients to regularize the interplay of influence among samples by projecting gradients onto the normal plane of the gradients to be retained. Our work is agnostic to statistics of the removal samples, outperforming existing baselines while providing theoretical analysis for the first time in unlearning a generative model.

翻译：近期关于被遗忘权的法规激发了对预训练机器学习模型遗忘处理的大量兴趣。在近似一种直接但昂贵的从头训练方法的同时，近期机器学习遗忘方法通过更新权重以消除样本对权重参数的影响来实现样本遗忘。本文提出了一种简单而有效的方法来消除数据对深度生成模型的影响。受多任务学习研究的启发，我们提出通过将梯度投影到待保留梯度的法平面上来操纵梯度，以规范样本间影响的相互作用。我们的方法对移除样本的统计特性不敏感，在性能上优于现有基准方法，同时首次为生成模型遗忘提供了理论分析。