Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity

arXiv:2605.22871v1 Announce Type: new Abstract: 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 retrai

Source: arXiv cs.LG — read the full report at the original publisher.

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