STDA-Net achieves 89.03% average accuracy and 87.64% macro F1 in cross-dataset sleep staging by processing 2D spectrograms with temporal modeling and unsupervised adversarial alignment, outperforming 1D baselines with lower variance.
Cross-language speech emotion recog- nition using bag-of-word representations, domain adaptation, and data augmentation
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Demographic-stratified fine-tuning of a convolutional recurrent sleep staging model improves Cohen's kappa by 0.9-12.9% over a single population-agnostic baseline on 100 clinical PSG recordings.
Supervised domain adaptation is required for any functional building damage detection across disasters, reaching Macro-F1 0.5552 on unseen Ida-BD data when combined with unsharp masking.
citing papers explorer
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STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification
STDA-Net achieves 89.03% average accuracy and 87.64% macro F1 in cross-dataset sleep staging by processing 2D spectrograms with temporal modeling and unsupervised adversarial alignment, outperforming 1D baselines with lower variance.
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Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography
Demographic-stratified fine-tuning of a convolutional recurrent sleep staging model improves Cohen's kappa by 0.9-12.9% over a single population-agnostic baseline on 100 clinical PSG recordings.
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Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation
Supervised domain adaptation is required for any functional building damage detection across disasters, reaching Macro-F1 0.5552 on unseen Ida-BD data when combined with unsharp masking.