通过自模式连通性引导的流形表示遗忘实现近似机器遗忘
Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity
AI总结：
提出ManiF-SMC方法，通过将遗忘样本从其原始流形表示质心推向保留数据中最近的语义邻居，并利用自模式连通性模块自适应生成边界，在表示空间内实现近似遗忘，无需依赖标签和任务梯度。
摘要：
机器遗忘是强制执行被遗忘权的基本机制。现有的依赖标签操作或任务梯度反转的遗忘研究通常遗忘效果有限，且可能破坏原始学习目标，通常不能保证与重新训练的标准遗忘等价。本文提出ManiF-SMC（自模式连通性引导的流形遗忘），其动机是观察到在剩余数据上重新训练的模型倾向于根据保留数据中的语义相似性对擦除样本进行分类。我们首先系统地将近似遗忘重新表述为：将每个擦除样本从其原始学习的流形表示质心推向保留数据中最近的语义邻居。这种重新表述使遗忘与重新训练行为对齐，并且仅在表示空间中操作，减少了对标签和任务特定梯度的依赖。为了解决基于流形表示的遗忘问题，ManiF-SMC将遗忘和表示保留目标封装在基于边界的三元组损失中。由于为遗忘找到合适的边界具有挑战性，我们提出一个自模式连通性模块，快速重建局部流形以指导每个遗忘案例的自适应边界生成。在四个代表性数据集上的大量实验表明，ManiF-SMC在仅操作模型表示空间的情况下，实现了与最先进近似方法相当的遗忘效果。
英文摘要：
Machine unlearning is a fundamental mechanism that enforces the right to be forgotten. Existing unlearning studies that rely on label manipulation or task-gradient reversal often deliver limited unlearning effectiveness. Moreover, they can undermine the original learning objective and typically do not guarantee equivalence to standard unlearning by retraining. In this paper, we propose \textbf{ManiF-SMC} (\textbf{Mani}fold \textbf{F}orgetting with \textbf{S}elf \textbf{M}ode \textbf{C}onnectivity), motivated by the observation that a model retrained on the remaining data tends to classify erased samples by their semantic similarity to the retained data. We begin with systematically recasting the approximate unlearning as pushing each erased sample away from its original learned manifold representation centroid toward its nearest semantic neighbors in the retained data. This reformulation aligns unlearning with retraining behavior and operates purely in representation space, reducing reliance on labels and task-specific gradients. To tackle the manifold representation-based unlearning problem, ManiF-SMC encapsulates the unlearning and representation preservation goals in a margin-based triplet loss. Because finding a suitable margin for unlearning is challenging, we propose a self-mode-connectivity module that rapidly reconstructs the local manifold to guide the adaptive margins generation for each unlearning case. Extensive experiments on four representative datasets show that ManiF-SMC achieves unlearning effectiveness comparable to state-of-the-art approximate methods while operating solely within the model's representation space.
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。
