Presents the first unsupervised source-free framework for ranking semantic and instance segmentation models via prediction consistency under perturbations, with rankings correlating to target-domain performance across 2D/3D biomedical tasks.
A General Framework for Uncertainty Estimation in Deep Learning.IEEE Robotics and Automation Letters, 5(2):3153– 3160
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Unsupervised Source-Free Ranking of Biomedical Segmentation Models Under Distribution Shift
Presents the first unsupervised source-free framework for ranking semantic and instance segmentation models via prediction consistency under perturbations, with rankings correlating to target-domain performance across 2D/3D biomedical tasks.