  We propose a novel method to train deep convolutional neural networks which
learn from multiple data sets of varying input sizes through weight sharing.
This is an advantage in chemometrics where individual measurements represent
exact chemical compounds and thus signals cannot be translated or resized
without disturbing their interpretation. Our approach show superior performance
compared to transfer learning when a medium sized and a small data set are
trained together. While we observe a small improvement compared to individual
training when two medium sized data sets are trained together, in particular
through a reduction in the variance.
