Generative digital humanities - Offert, Fabian; Bell, Peter (CEUR Workshop Proceedings, 2020)
While generative machine learning has recently attracted a significant amount of attention in the computer science community, its potential for the digital humanities has so far not been fully evaluated. In this paper, we examine generative adversarial networks, a state-of-the art generative machine learning technique. We argue that GANs can be particularly useful in digital art history, where they can be employed to facilitate the exploration of the semantic structure of large image corpora. Moreover, we posit that the foundational statistical distinction between discriminative and generative approaches offers an alternative critical perspective on machine learning in the digital humanities context. If "all models are wrong, some are useful", as the often-cited passage reads, we argue that, in case of the digital humanities, the most useful-wrong models are generative.
CRIS-Team Scopus Importer:2020-11-20
Keywords: Data augmentation; Digital art history; Generative models; Machine learning