A masked autoencoder trained to reconstruct randomly masked Raman spectra produces representations that enable 80.6% unsupervised clustering and 83.9% fine-tuned classification on 30 bacterial classes.
Pretreatment-free SERS sensing of microplastics using a self- attention-based neural network on hierarchically porous Ag foams
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A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders
A masked autoencoder trained to reconstruct randomly masked Raman spectra produces representations that enable 80.6% unsupervised clustering and 83.9% fine-tuned classification on 30 bacterial classes.