Prefer-DAS integrates self-training, prompt-guided contrastive learning, local direct preference optimization (LPO), and unsupervised preference optimization (UPO) to achieve effective domain adaptive segmentation in electron microscopy using sparse prompts and local preferences.
Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results
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Neighbor-consistent training reduces RMS spatial variation rates in personal sound zone isolation by up to 55.9% (woofer) and 30.3% (tweeter) in simulation and up to 61.8% in measurements while preserving isolation quality.
citing papers explorer
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Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy
Prefer-DAS integrates self-training, prompt-guided contrastive learning, local direct preference optimization (LPO), and unsupervised preference optimization (UPO) to achieve effective domain adaptive segmentation in electron microscopy using sparse prompts and local preferences.
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Neighbor-Consistent Neural Filters for Robust Personal Sound Zones Under Localization Uncertainty
Neighbor-consistent training reduces RMS spatial variation rates in personal sound zone isolation by up to 55.9% (woofer) and 30.3% (tweeter) in simulation and up to 61.8% in measurements while preserving isolation quality.