Not All Samples Leave Equal Imprints: Influence-Aware Unlearning in Continual Learning

Machine unlearning aims to remove the influence of specific data from a trained model, motivated by data regulations such as the right to be forgotten. Existing methods implicitly assume that all samples are encoded with similar strength, ignoring differences in their imprint on the model, and therefore apply uniform unlearning updates. This assumption breaks in class-incremental learning (CIL), where training data leave highly non-uniform imprints on the model. As a result, uniform unlearning leads to over-removal of weakly encoded samples and insufficient forgetting of strongly encoded ones, degrading overall performance. To address this, we propose IMPRINT, an imprint-aware unlearning framework for CIL. Our key idea is to align unlearning intensity with sample-wise imprint, estimated via the logit margin as a simple yet effective proxy. This enables adaptive scaling of the forget loss and seamless plug-and-play integration with existing unlearning methods. Extensive experiments show that IMPRINT consistently improves both forgetting and retention performance in CIL settings, significantly reducing the gap to retraining while outperforming state-of-the-art baselines.