研究论文区分了用于人工智能不确定性的交叉验证与深度集成

报道来源 [3]

1. arXiv cs.LG TIER_1 English(EN) · Tristan Kirscher (ICube, Institut Strauss, DKFZ), Markus Bujotzek (DKFZ), Yannick Kirchhoff (DKFZ), Maximilian Rokuss (DKFZ), Fabian Isensee (DKFZ), Kim-Celine Kahl (DKFZ), Balint Kovacs (DKFZ), Klaus Maier-Hein (DKFZ) · 2026-05-25 04:00

   Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation

   arXiv:2605.18329v2 Announce Type: replace-cross Abstract: Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV), yet refer to them as ``deep ensembles'' (D…

2. Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-18 12:46

   Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation

   Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV), yet refer to them as ``deep ensembles'' (DE). Because CV members are trained on different data subse…

3. arXiv cs.CV TIER_1 English(EN) · Maier-Hein Klaus · 2026-05-18 12:46

   Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation

   Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV), yet refer to them as ``deep ensembles'' (DE). Because CV members are trained on different data subse…