PATCH: a deep learning method to assess heterogeneity of artistic practice in historical paintings

TL;DR

PATCH is a novel deep learning approach that identifies individual artistic practices in historical paintings without external training data, revealing insights into workshop dynamics and artistic heterogeneity.

Contribution

This paper introduces PATCH, a new machine learning method capable of classifying artistic practice regimes in paintings without needing external ground truth data.

Findings

Successfully applied to El Greco's paintings, revealing new insights into workshop practices.

Outperforms traditional statistical and unsupervised machine learning methods.

Provides a quantitative measure of artistic heterogeneity across artworks.

Abstract

The history of art has seen significant shifts in the manner in which artworks are created, making understanding of creative processes a central question in technical art history. In the Renaissance and Early Modern period, paintings were largely produced by master painters directing workshops of apprentices who often contributed to projects. The masters varied significantly in artistic and managerial styles, meaning different combinations of artists and implements might be seen both between masters and within workshops or even individual canvases. Information on how different workshops were managed and the processes by which artworks were created remains elusive. Machine learning methods have potential to unearth new information about artists' creative processes by extending the analysis of brushwork to a microscopic scale. Analysis of workshop paintings, however, presents a challenge in that documentation of the artists and materials involved is sparse, meaning external examples are not available to train networks to recognize their contributions. Here we present a novel machine learning approach we call pairwise assignment training for classifying heterogeneity (PATCH) that is capable of identifying individual artistic practice regimes with no external training data, or "ground truth." The method achieves unsupervised results by supervised means, and outperforms both simple statistical procedures and unsupervised machine learning methods. We apply this method to two historical paintings by the Spanish Renaissance master, El Greco: The Baptism of Christ and Christ on the Cross with Landscape, and our findings regarding the former potentially challenge previous work that has assigned the painting to workshop members. Further, the results of our analyses create a measure of heterogeneity of artistic practice that can be used to characterize artworks across time and space.