iSAGE achieves near-dense mIoU performance in remote sensing semantic segmentation using iterative expert clicks on confident model errors with an error-weighted loss, using only 0.011-0.04% of pixels.
Rethinking aleatoric and epistemic uncer- tainty
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Decision-theoretic uncertainty quantification formalizes evaluation of generative domain adaptation trustworthiness for PPG-based atrial fibrillation classification by linking uncertainty to downstream task utility.
The paper shows that encoder dropout variance dominates under covariate shift in twin networks and better predicts errors on out-of-distribution samples than head variance.
citing papers explorer
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iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision
iSAGE achieves near-dense mIoU performance in remote sensing semantic segmentation using iterative expert clicks on confident model errors with an error-weighted loss, using only 0.011-0.04% of pixels.
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Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification
Decision-theoretic uncertainty quantification formalizes evaluation of generative domain adaptation trustworthiness for PPG-based atrial fibrillation classification by linking uncertainty to downstream task utility.
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Localising Dropout Variance in Twin Networks
The paper shows that encoder dropout variance dominates under covariate shift in twin networks and better predicts errors on out-of-distribution samples than head variance.