Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery.

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 pottery vessels from the collection of the Museum of London.