Can AI Recognize the Style of Art? Analyzing Aesthetics through the Lens of Style Transfer

2025-04-19

AI Summary

Current AI approaches exhibit limitations in art style recognition and aesthetic analysis, particularly regarding interpretability and semantic alignment with subjective human judgment. Method: This work pioneers the adaptation of style transfer techniques—from generative image synthesis to interpretable aesthetic analysis—by establishing a comparative experimental framework integrating CNNs (VGG) and vision transformers (ViT). Leveraging formal aesthetic theory, feature visualization, and cross-architectural analysis, it systematically dissects modeling mechanisms for texture, composition, and color harmony. Contribution/Results: It establishes an interpretable mapping between AI representations and artistic style elements; proposes a novel evaluation paradigm oriented toward aesthetic understanding; and reveals fundamental bottlenecks in semantic-level style disentanglement and subjective aesthetic consistency. Empirical results demonstrate significant architectural disparities in modeling distinct stylistic attributes, thereby providing both theoretical foundations and practical guidelines for aesthetics-driven AI design.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: AI for Accessibility
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• AI's ability to recognize artistic style via transfer
• Comparing CNN and Transformer style transfer aesthetics
• Identifying limitations and future directions in style transfer

Innovation

• Uses CNN and Transformer for style transfer
• Analyzes style through aesthetic perspective
• Proposes future directions for style transfer