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.
How Far Pre-trained Models Are from Neural Collapse on the Target Dataset Informs their Trans- ferability
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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.