Using a CNN Model to Assess Paintings' Creativity
This provides a more efficient alternative to traditional methods for assessing artistic creativity, though it is incremental as it extends existing ML approaches from drawings to paintings.
The researchers tackled the problem of automatically assessing creativity in paintings by developing a CNN model, which achieved 90% accuracy on a dataset of 600 paintings and was faster than human raters.
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.