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DISCERN: Distributional Synthetic Replay for Incremental Machine Unlearning

DISCERN is a new method for incremental class unlearning that removes classes from a model sequentially without access to the retain set. It fits per-class parametric distributions to synthetic embeddings, selecting the best family via statistical tests and cross-validated log-likelihood. On transformer backbones, DISCERN remains stable after 20 sequential deletions, while a strong simultaneous baseline collapses. The method reduces resurfacing of forgotten classes, acting as a stabilizer of accumulated forgetting.

Why it matters: This paper introduces a novel approach to a practical problem (incremental unlearning) with strong empirical results on transformers, offering clear value for researchers and practitioners.