Weight Sharing And Deep Learning For Spectral Data
Jacob Søgaard Larsen, Line Clemmensen
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04 May 2020
We propose a novel method to co-train deep convolutional neural networks for data sets of differing position specific data. This is an advantage in chemometrics where individual measurements represent exact chemical compounds, e.g. for given wavelengths, and thus signals cannot be translated or resized without disturbing their interpretation. Our approach outperforms transfer learning for three small data sets co-trained with a medium sized data set.
