Reference-Guided Machine Unlearning
Machine unlearning aims to remove the influence of specific training data from a model while preserving its general utility. In vision, many approximate unlearning methods pursue this goal through degradation-based heuristics, such as loss maximization or random labeling. Yet making a model worse on forget samples is not the same as making it behave as if those examples had never been seen: these signals can be poorly conditioned, destabilize optimization, and harm generalization. We argue that approximate unlearning should instead prioritize distributional indistinguishability, aligning the model's predictive behavior on forget data with that on truly unseen data. Motivated by this princ...
| Authors | Jonas Mirlach, Sonia Laguna, Julia E. Vogt |
|---|---|
| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 29 September 2026 |
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