Author

Charmaine Barker

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Preprint Oct 2026

Localisation-Aware Uncertainty for Pretrained Object Detection

Reliable uncertainty estimation is essential for deploying object detectors when distribution/covariate shift and adversarial attacks may occur. Existing approaches often require detector retraining, architectural modification, or repeated inference, which may be infeasible or incur significant overheads. We introduce...

Charmaine Barker, Daniel Bethell, Simos Gerasimou · 0 citations

#machine learning Preprint Oct 2026

Repurposing Obsolete Representations for Post-Deployment Adaptation

Deep Repurposing (DR) is proposed, a post-hoc framework for adapting models under task obsolescence that matches or exceeds competing unlearning and editing baselines in retained accuracy, eliminates obsolete predictions, and adapts up to $60\times$ faster than competing unlearning methods.

Daniel Bethell, Charmaine Barker, Simos Gerasimou · 0 citations

#machine learning Preprint Oct 2026

Robust Evidential Learning Through Latent Consistency

Reliable uncertainty quantification is essential for deploying deep learning models in high-stakes settings, where out-of-distribution and adversarial inputs can induce confident but unreliable predictions. Evidential Deep Learning provides efficient uncertainty estimates in a single forward pass, but can still assign...

Charmaine Barker, Daniel Bethell, Simos Gerasimou · 0 citations