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Article

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

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School of Mathematics and Actuarial Science, University of Leicester, Leicester LE1 7RH, UK
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School of Archaeology and Ancient History, University of Leicester, Leicester LE1 7RH, UK
3
Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, 603105 Nizhni Novgorod, Russia
*
Author to whom correspondence should be addressed.
Academic Editor: Gholamreza Anbarjafari
Entropy 2021, 23(9), 1140; https://doi.org/10.3390/e23091140
Received: 8 June 2021 / Revised: 12 August 2021 / Accepted: 17 August 2021 / Published: 31 August 2021
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. View Full-Text
Keywords: deep learning; machine learning; image classification; simulated data; learning from scarce information; Roman pottery (terra sigillata) deep learning; machine learning; image classification; simulated data; learning from scarce information; Roman pottery (terra sigillata)
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MDPI and ACS Style

Núñez Jareño, S.J.; van Helden, D.P.; Mirkes, E.M.; Tyukin, I.Y.; Allison, P.M. Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery. Entropy 2021, 23, 1140. https://doi.org/10.3390/e23091140

AMA Style

Núñez Jareño SJ, van Helden DP, Mirkes EM, Tyukin IY, Allison PM. Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery. Entropy. 2021; 23(9):1140. https://doi.org/10.3390/e23091140

Chicago/Turabian Style

Núñez Jareño, Santos J., Daniël P. van Helden, Evgeny M. Mirkes, Ivan Y. Tyukin, and Penelope M. Allison 2021. "Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery" Entropy 23, no. 9: 1140. https://doi.org/10.3390/e23091140

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