Machine unlearning as an emerging research topic for data regulations, aims to adjust a trained model to approximate a retrained one that excludes a portion of training data. Previous studies showed that class-wise unlearning is successful in forgetting the knowledge of a target class, through gradient ascent on the forgetting data or fine-tuning with the remaining data. However, while these methods are useful, they are insufficient as the class label and the target concept are often considered to coincide. In this work, we decouple them by considering the label domain mismatch and investigate three problems beyond the conventional all matched forgetting, e.g., target mismatch, model mismatch, and data mismatch forgetting. We systematically analyze the new challenges in restrictively forgetting the target concept and also reveal crucial forgetting dynamics in the representation level to realize these tasks. Based on that, we propose a general framework, namely, TARget-aware Forgetting (TARF). It enables the additional tasks to actively forget the target concept while maintaining the rest part, by simultaneously conducting annealed gradient ascent on the forgetting data and selected gradient descent on the hard-to-affect remaining data. Empirically, various experiments under the newly introduced settings are conducted to demonstrate the effectiveness of our TARF.

翻译：机器遗忘作为应对数据法规的新兴研究课题，旨在调整已训练模型以近似重新训练的模型，从而排除部分训练数据。先前研究表明，通过遗忘数据的梯度上升或剩余数据的微调，类别级遗忘能有效遗忘目标类别的知识。然而，这些方法虽有用但存在不足，因为类别标签与目标概念常被视为等同。本文通过考虑标签域不匹配来解耦二者，并研究了传统全匹配遗忘之外的三个问题，例如目标不匹配、模型不匹配和数据不匹配遗忘。我们系统分析了限制性遗忘目标概念的新挑战，并在表征层面揭示了实现这些任务的关键遗忘动态。基于此，我们提出通用框架——目标感知遗忘（TARF）。该框架通过同时对遗忘数据进行退火梯度上升和对难以影响的剩余数据进行选择性梯度下降，使额外任务能够主动遗忘目标概念同时保留其余部分。实验方面，我们在新引入的设置下进行了多种实验，验证了TARF的有效性。