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arXiv Machine Learning By Daniel Bethell, Charmaine Barker, Simos Gerasimou

Repurposing Obsolete Representations for Post-Deployment Adaptation

Read the original on arXiv Machine Learning →

Deep Repurposing (DR) is a post‑hoc framework that adapts deep neural networks when parts of their output space become obsolete after deployment. DR estimates the latent geometry of obsolete and retained regions, removes components that support obsolete outputs, and reallocates retained-compatible evidence through an analytic repair map without gradient updates. The method yields repaired predictions and representations that eliminate obsolete outputs while preserving or improving retained accuracy, and it adapts up to 60× faster than competing unlearning methods.

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 Machine Learning.

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