在机器遗忘中解耦类别标签与目标概念
Decoupling the Class Label and the Target Concept in Machine Unlearning
AI总结：
本研究针对机器遗忘中类别标签与目标概念默认绑定的局限，将二者解耦并提出三类新遗忘问题，通过分析表征层遗忘动态，提出目标感知遗忘框架TARF，可在维持其余性能的同时实现目标概念的限制性遗忘，经新设置实验验证了有效性。
摘要：
机器遗忘是面向数据监管的新兴研究方向，旨在调整已训练模型，使其逼近排除部分训练数据后重新训练的模型。已有研究表明，通过对遗忘数据执行梯度上升或用剩余数据微调，类别级遗忘可成功遗忘目标类别的知识。然而，这些方法存在局限性——它们通常默认类别标签与目标概念是一致的。本研究考虑标签域不匹配的情况，将二者解耦，探究了三类超出传统全匹配遗忘范畴的问题：目标不匹配遗忘、模型不匹配遗忘与数据不匹配遗忘。我们系统分析了限制性遗忘目标概念面临的新挑战，还在表征层面揭示了实现这些任务的关键遗忘动态机制。在此基础上，我们提出了通用框架TARget-aware Forgetting（TARF，目标感知遗忘框架）。该框架通过同时对遗忘数据执行退火梯度上升、对难以影响的剩余数据执行选择性梯度下降，可在维持模型其余部分性能的同时，主动完成目标概念遗忘的新增任务。实验方面，我们在新提出的多种设置下开展了大量实验，验证了TARF的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
