FUSED integrates EEG foundation models into source-free domain adaptation via dual-branch co-adaptation, consensus filtering, and two-stage pseudo-label refinement to achieve state-of-the-art cross-subject EEG decoding.
Attracting and dispersing: A simple approach for source-free domain adaptation
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SERE is a new semi-supervised method for cross-lingual speech emotion recognition that needs only 5-shot source labels and no target labels or translations by using resonance embeddings and interaction losses.
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Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
FUSED integrates EEG foundation models into source-free domain adaptation via dual-branch co-adaptation, consensus filtering, and two-stage pseudo-label refinement to achieve state-of-the-art cross-subject EEG decoding.
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Semantic-Emotional Resonance Embedding: A Semi-Supervised Paradigm for Cross-Lingual Speech Emotion Recognition
SERE is a new semi-supervised method for cross-lingual speech emotion recognition that needs only 5-shot source labels and no target labels or translations by using resonance embeddings and interaction losses.