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arXiv AI By Nicol\'as Segura-Kunsagi, Juan Luis Su\'arez-D\'iaz, Iv\'an Sevillano-Garc\'ia, Francisco Herrera

DISCERN: Distributional Synthetic Replay for Retain-Free Incremental Machine Unlearning

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DISCERN is a class‑unlearning method that replaces real data with synthetic embeddings drawn from per‑class parametric distributions, selecting the best fit via statistical tests or cross‑validated likelihood. Unlike prototype replay, it models both Euclidean (Gaussian, Laplace, Student’s t) and directional (von Mises–Fisher, Fisher–Bingham) families to capture the latent geometry of transformer embeddings, which lie on a hypersphere. Experiments on MNIST, CIFAR‑10, and CIFAR‑100 with convolutional and transformer backbones show DISCERN performs competitively in simultaneous deletions and remains the most stable in incremental class unlearning, especially on ViT‑B/16 where it maintains a high harmonic mean after 20 sequential deletions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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