Adapted DINOv2 generalises better than task-specific medical segmentation nets
Only a small part of DINOv2 was adapted; in-distribution U-Nets stayed sharper and caught small lesions more often.
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Only a small part of DINOv2 was adapted; in-distribution U-Nets stayed sharper and caught small lesions more often.
CAT-CCB and CAT-CMDP use causal transportability and pessimistic optimization, with finite-sample suboptimality bounds.
The leaderboard uses JUMP-CP Cell Painting images from unseen experimental batches and data sources.
Exact minimax limits show linear shift repair needs far more calibration signal than detection.
AdaptVPR generates route-aware synthetic hard positives for visual place recognition, releasing the 160K-image AdaptCities dataset with R@1 gains up to 9.2% under domain shift.
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