The nexus between archaeology and imagination has received significant attention in the last two decades. Definitions of 'archaeological imagination' range from a 'way of being attuned to the world' (Thomas 1999, 63) and, therefore, able to 'read' the past as a hunter 'reads' the tracks of a prey, to 'a creative impulse and faculty at the heart of archaeology' (Shanks 2012, 25). Collaboration between artists (mostly visual artists) and archaeologists has a long and established tradition, but an understandable concern for the risks of an 'imaginative' archaeology has prevented full exploration of the possible overlap of these two roles. This paper investigates whether imagination and art have a positive impact on archaeological research, building up from the collaborative experience of two scholars who are also creative artists. Through a project that combines creative writing, graphic art, material culture and landscape and incorporates both creative work and reflective practice, the authors address the multifaceted challenges of representing and interpreting the past in and outside an academic context.
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.
Integrating qualitative and social science factors in archaeological modelling aims to contribute to bridging the gap between the subdiscipline of archaeological computational modelling and wider archaeology. Agent-based modelling (ABM: a modelling approach based on simulating many autonomous agents to observe the aggregate results of their behaviour) largely acts as the lens through which the overarching problem of the incorporation of social and qualitative factors into computational models is investigated. The details may therefore be unique to ABM, but wider conceptual considerations apply to all computational modelling.
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