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Physics-Aware Self-Supervised Pretraining on Multi-Domain Raman Spectra
Raman spectroscopy generates large volumes of unlabeled data that are difficult to exploit with conventional supervised learning. This work introduces a multi-domain self-supervised learning framework based on a masked autoencoder (MAE) for learning transferable Raman spectral representations from h
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