The mutual-information measure of epistemic uncertainty is not reducible by additional data, requiring a split into aleatoric, sample-reducible epistemic, and mechanism-reducible epistemic uncertainty.
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μMatch applies student-teacher semi-supervised methods with foundation models to improve segmentation of mitochondria, nuclei, and neurites in EM images over strong baselines.
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$\mu$Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM
μMatch applies student-teacher semi-supervised methods with foundation models to improve segmentation of mitochondria, nuclei, and neurites in EM images over strong baselines.