Learning from scarce information: using synthetic data to classify Roman fine ware pottery

TL;DR

This paper presents a transfer learning approach using synthetic data generated from expert knowledge to improve classification of Roman pottery from limited photographic datasets.

Contribution

It introduces a hybrid method combining synthetic data generation with fine-tuning to enhance classifier generalization on scarce archaeological data.

Findings

Synthetic data improves classification accuracy.

Hybrid approach outperforms models trained only on real data.

Method is robust across different architectures and conditions.

Abstract

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.