  Transfer learning allows us to train deep architectures requiring a large
number of learned parameters, even if the amount of available data is limited,
by leveraging existing models previously trained for another task. Here we
explore the applicability of transfer learning utilizing models pre-trained on
non-software engineering data applied to the problem of classifying software
UML diagrams. Our experimental results show training reacts positively to
transfer learning as related to sample size, even though the pre-trained model
was not exposed to training instances from the software domain. We contrast the
transferred network with other networks to show its advantage on different
sized training sets, which indicates that transfer learning is equally
effective to custom deep architectures when large amounts of training data is
not available.
