CoUn: Empowering Machine Unlearning via Contrastive Learning

In three linesCoUn is a machine unlearning method using contrastive learning to remove the influence of specific data from trained models. The technique adjusts learned representations using only retain data, outperforming existing label manipulation and weight perturbation baselines across multiple datasets and architectures.

Summary generated by Claude — human-verified