This study investigates how artificial intelligence (AI) recognizes style through style transfer-an AI technique that generates a new image by applying the style of one image to another. Despite the considerable interest that style transfer has garnered among researchers, most efforts have focused on enhancing the quality of output images through advanced AI algorithms. In this paper, we approach style transfer from an aesthetic perspective, thereby bridging AI techniques and aesthetics. We analyze two style transfer algorithms: one based on convolutional neural networks (CNNs) and the other utilizing recent Transformer models. By comparing the images produced by each, we explore the elements that constitute the style of artworks through an aesthetic analysis of the style transfer results. We then elucidate the limitations of current style transfer techniques. Based on these limitations, we propose potential directions for future research on style transfer techniques.
View on arXiv@article{yeo2025_2504.14272,
title={ Can AI Recognize the Style of Art? Analyzing Aesthetics through the Lens of Style Transfer },
author={ Yunha Yeo and Daeho Um },
journal={arXiv preprint arXiv:2504.14272},
year={ 2025 }
}