Forget the Data, Keep the Capability: Switchable Unlearning for Retrieval

The paper introduces MoME, a switchable unlearning framework for re-identification models that allows a single backbone to operate in either Full mode (standard inference) or Core mode (unlearned inference). MoME partitions the backbone into core parameters that retain retrieval capability and zero‑initialized parasitic parameters that store full‑data knowledge, training them with gradient isolation so the core never receives forget‑set gradients. The authors demonstrate that Core mode satisfies deletion requests with a simple flag flip, prove its independence from the forget set, and validate its effectiveness on Market‑1501, VeRi‑776, and RegDB using ResNet and CLIP backbones.

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