[HTML][HTML] Realizing transfer learning for updating deep learning models of spectral data to be used in new scenarios
This study presents the concept of transfer learning (TL) to the chemometrics community for
updating DL models related to spectral data, particularly when a pre-trained DL model
needs to be used in a scenario having unseen variability. This is the typical situation where
classical chemometrics models require some form of re-calibration or update. In TL, the
network architecture and weights from the pre-trained DL model are complemented by
adding extra fully connected (FC) layers when dealing with the new data. Such extra FC …
updating DL models related to spectral data, particularly when a pre-trained DL model
needs to be used in a scenario having unseen variability. This is the typical situation where
classical chemometrics models require some form of re-calibration or update. In TL, the
network architecture and weights from the pre-trained DL model are complemented by
adding extra fully connected (FC) layers when dealing with the new data. Such extra FC …
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