1 citations · 2 across the 5 of their papers we have counts for
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Repurposing Obsolete Representations for Post-Deployment Adaptation
Daniel Bethell, Charmaine Barker, Simos Gerasimou
Deep neural networks are increasingly deployed in long-lived systems, where task requirements may change after training. In such settings, part of the original output space may bec…
Localisation-Aware Uncertainty for Pretrained Object Detection
Charmaine Barker, Daniel Bethell, Simos Gerasimou
Reliable uncertainty estimation is essential for deploying object detectors when distribution/covariate shift and adversarial attacks may occur. Existing approaches often require d…
Robust Evidential Learning Through Latent Consistency
Charmaine Barker, Daniel Bethell, Simos Gerasimou
Reliable uncertainty quantification is essential for deploying deep learning models in high-stakes settings, where out-of-distribution and adversarial inputs can induce confident b…