Speakers
Description
This paper will showcase multiple computational approaches to explore the pictorial conventions of landscape paintings in the late 19th century, taking a comparative approach between collections from Japan, China, and the UK. These datasets will be the basis of an investigation into the use of latent space to explore cultural frameworks within museum collections.
The use of such models raises a number of methodological issues for researchers. Most notably, they can introduce – or exacerbate – biases within datasets (Bode, 2020). Concerningly, these biases are highly variable and difficult to predict. As a result, it can be difficult to separate the historical patterns of interest from the impact of the model’s training data and architecture. We propose a new line of inquiry focusing on latent spaces as a place to quantify these distortions. During the talk we will explore the limitations of these models within digital art history; what can or cannot be projected in the latent space? What is amplified by the model, and what is underrepresented? And how do changes while training a model shift the relationships between artworks and our consequent understanding of the collections? Ultimately, we will highlight the trade-off of various approaches, exploring which methods are most appropriate for different forms of cultural analysis.
Therefore, our study of landscape paintings will form the foundation for a methodological critique as we attempt to open new avenues of inquiry for exploring similarity within collections.
Referenced Works
Bode, K. (2020) ‘Why You Can’t Model Away Bias’, Modern Language Quarterly, 81(1), pp. 95–124. Available at: https://doi.org/10.1215/00267929-7933102.