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

This paper introduces ManiF-SMC, a novel method for approximate machine unlearning that addresses limitations of existing approaches. It reformulates unlearning as moving erased samples' manifold representations toward retained data's semantic neighbors, aiming for equivalence with retraining.