A self-supervised framework that adds AE-GAN reconstruction error from external healthy data to multi-view contrastive learning improves low-label EEG and ECG disease classification in reported experiments.
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A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series
A self-supervised framework that adds AE-GAN reconstruction error from external healthy data to multi-view contrastive learning improves low-label EEG and ECG disease classification in reported experiments.