μMatch applies student-teacher semi-supervised methods with foundation models to improve segmentation of mitochondria, nuclei, and neurites in EM images over strong baselines.
Adamatch: A unified approach to semi-supervised learning and domain adaptation,
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PRISM combines data-dependent channel weighting via expert ensemble and confidence-filtered pseudo-label domain adaptation to outperform prior methods on cross-subject EEG emotion tasks in DEAP, DREAMER, and SEED.
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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.