Jumping into the artistic deep end: building the catalogue raisonné

Abstract

The catalogue raisonné compiled by art scholars holds information about an artist’s work such as a painting’s image, medium, provenance, and title. The catalogue raisonné as a tangible asset suffers from the challenges of art authentication and impermanence. As the catalogue raisonné is born digital, the impermanence challenge abates, but the authentication challenge persists. With the popularity of artificial intelligence and its deep learning architectures of computer vision, we propose to address the authentication challenge by creating a new artefact for the digital catalogue raisonné: a digital classification model. This digital classification model will help art scholars with new artwork claims via a tool that authenticates a proposed artwork with an artist. We create this tool by training a machine learning model with 90 artists having at least 150 artworks and achieve an accuracy of 87.31%. We use the ResNet Convolutional Neural Network to improve accuracy and number of artist classes over state-of-the-art artist classification experiments using the WikiArt database. We address inconsistencies in the way scholars approach artist classification by providing a consistent method to recreate our dataset and providing a consistent method to calculate performance metrics based on imbalanced data.