Assessing artistic creativity has long challenged researchers, with traditional methods proving time-consuming. Recent studies have applied machine learning to evaluate creativity in drawings, but not paintings. Our research addresses this gap by developing a CNN model to automatically assess the creativity of human paintings. Using a dataset of six hundred paintings by professionals and children, our model achieved 90% accuracy and faster evaluation times than human raters. This approach demonstrates the potential of machine learning in advancing artistic creativity assessment, offering a more efficient alternative to traditional methods.
@article{arxiv.2408.01481,
title = {Using a CNN Model to Assess Paintings' Creativity},
author = {Zhehan Zhang and Meihua Qian and Li Luo and Qianyi Gao and Xianyong Wang and Ripon Saha and Xinxin Song},
journal= {arXiv preprint arXiv:2408.01481},
year = {2025}
}