  The models and weights of prior trained Convolutional Neural Networks (CNN)
created to perform automated isotopic classification of time-sequenced
gamma-ray spectra, were utilized to provide source domain knowledge as training
on new domains of potential interest. The previous results were achieved solely
using modeled spectral data. In this work we attempt to transfer the knowledge
gained to the new, if similar, domain of solely measured data. The ability to
train on modeled data and predict on measured data will be crucial in any
successful data-driven approach to this problem space.
