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

In three linesManiF-SMC proposes machine unlearning via manifold representation forgetting with adaptive margin-based triplet loss guided by self-mode connectivity. The method pushes erased samples away from original learned manifold centroids toward retained data neighbors, operating purely in representation space. Experiments on 4 datasets match state-of-the-art approximate unlearning effectiveness.

Summary generated by Claude — human-verified