Machine unlearning aims to reduce the influence of designated training data from a trained model while preserving its utility on retained data. Identity-level unlearning is particularly challenging when only limited target-specific information is available when a forgetting request is processed. In this work, we propose Memory-Coupled Unlearning (MCU), a framework that augments a host model with a Modern Hopfield Network (MHN) serving as a controllable external identity-prototype memory. During prototype construction, feature representations associated with each training identity are aggregated into identity prototypes and stored in the MHN. A Prototype Adaptation Network (PAN) transforms the retrieved memory representation, while a Dependency Gating Unit (DGU) controls its contribution to downstream prediction. When an identity is requested to be forgotten, MCU first removes the corresponding prototype from the external memory and subsequently readapts the model using limited support information associated with the target identities together with retained data. Importantly, in this work, ``data-absent'' specifically refers to the absence of the complete original forget set during request-time gradient-based readaptation; retained data may still be used to preserve downstream-task utility. Experiments based on the identity-partitioning and one-shot support construction of 1-SHUI show that MCU achieves a favorable empirical trade-off among model utility, membership distinguishability, and request-time computational efficiency compared with the evaluated baselines. These results provide empirical evidence for the potential of controllable external memory in machine unlearning, rather than a formal guarantee of complete information removal.

翻译：暂无翻译