arXiv: cs.AI preprintsInternational6 November 2026
DISCERN: Distributional Synthetic Replay for Retain-Free Incremental Machine Unlearning
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arXiv:2611.07892v1 Announce Type: cross Abstract: Machine unlearning aims to remove the influence of specific training data from a trained model without retraining from scratch. In real deployments, however, deletion requests arrive over time, calling for incremental class unlearning (ICU): classes are removed sequentially, previously forgotten knowledge must remain forgotten, and the remaining classes must be preserved. We present DIstributional SynthetiC rEplay for Retain-free iNcremental unlearning (DISCERN), an intervention-based class unlearning method that replaces access to the retain s
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