DLink distills layer-wise knowledge from EEG foundation models via a lightweight router and spectral alignment to produce compact students that narrow the gap to full EFMs on four benchmarks while reducing parameters, FLOPs, and inference latency.
CM- CRD: cross-modal contrastive representation distillation for emotion recognition.CoRR, abs/2504.09221
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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.
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DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models
DLink distills layer-wise knowledge from EEG foundation models via a lightweight router and spectral alignment to produce compact students that narrow the gap to full EFMs on four benchmarks while reducing parameters, FLOPs, and inference latency.
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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.