PGUDA uses pressure signals to train a teacher network that distills modality-invariant knowledge into an sEMG student via cross-modal distillation, reaching 58.08% cross-subject accuracy with only 5% labeled data for the teacher.
Adaptive batch normalization for practical domain adaptation,
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NEO performs test-time adaptation by re-centering target latent embeddings at the origin, boosting accuracy on distribution-shifted datasets like ImageNet-C with no optimization or hyperparameters and minimal extra compute.
Multi-purposing the domain discriminator to supply both domain-invariance and pseudo-label confidence scores in domain adaptation.
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PGUDA: Pressure-Guided Unsupervised Domain Adaptation with Cross-Modal Knowledge Distillation for sEMG-Based Gesture Recognition
PGUDA uses pressure signals to train a teacher network that distills modality-invariant knowledge into an sEMG student via cross-modal distillation, reaching 58.08% cross-subject accuracy with only 5% labeled data for the teacher.