Using a CNN Model to Assess Visual Artwork's Creativity
<|reference_start|>Using a CNN Model to Assess Visual Artwork's Creativity: 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.<|reference_end|>
@article{zhang2024using,
    title={Using a CNN Model to Assess Visual Artwork's Creativity},
    author={Zhehan Zhang, Meihua Qian, Li Luo, Ripon Saha, Qianyi Gao, Xinxin Song},
    journal={arXiv preprint arXiv:2408.01481},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01481},
    primaryClass={cs.CV cs.HC cs.LG}
}