  In this article we consider a version of the challenging problem of learning
from datasets whose size is too limited to allow generalisation beyond the
training set. To address the challenge we propose to use a transfer learning
approach whereby the model is first trained on a synthetic dataset replicating
features of the original objects. In this study the objects were smartphone
photographs of near-complete Roman terra sigillata pottery vessels from the
collection of the Museum of London. Taking the replicated features from
published profile drawings of pottery forms allowed the integration of expert
knowledge into the process through our synthetic data generator. After this
first initial training the model was fine-tuned with data from photographs of
real vessels. We show, through exhaustive experiments across several popular
deep learning architectures, different test priors, and considering the impact
of the photograph viewpoint and excessive damage to the vessels, that the
proposed hybrid approach enables the creation of classifiers with appropriate
generalisation performance. This performance is significantly better than that
of classifiers trained exclusively on the original data which shows the promise
of the approach to alleviate the fundamental issue of learning from small
datasets.
