Discovering the creative potentials of a random signal to various artistic
expressions in aesthetic and conceptual richness is a ground for the recent
success of generative machine learning as a way of art creation. To understand
the new artistic medium better, we conduct a comprehensive analysis to position
AI-generated art within the context of human art heritage. Our comparative
analysis is based on an extensive dataset, dubbed ``ArtConstellation,''
consisting of annotations about art principles, likability, and emotions for
6,000 WikiArt and 3,200 AI-generated artworks. After training various
state-of-the-art generative models, art samples are produced and compared with
WikiArt data on the last hidden layer of a deep-CNN trained for style
classification. We actively examined the various art principles to interpret
the neural representations and used them to drive the comparative knowledge
about human and AI-generated art. A key finding in the semantic analysis is
that AI-generated artworks are visually related to the principle concepts for
modern period art made in 1800-2000. In addition, through Out-Of-Distribution
(OOD) and In-Distribution (ID) detection in CLIP space, we find that
AI-generated artworks are ID to human art when they depict landscapes and
geometric abstract figures, while detected as OOD when the machine art consists
of deformed and twisted figures. We observe that machine-generated art is
uniquely characterized by incomplete and reduced figuration. Lastly, we
conducted a human survey about emotional experience. Color composition and
familiar subjects are the key factors of likability and emotions in art
appreciation. We propose our whole methodologies and collected dataset as our
analytical framework to contrast human and AI-generated art, which we refer to
as ``ArtNeuralConstellation''. Code is available at:
this https URL

通过比较分析 AI 生成的艺术与人类艺术，揭示了生成式机器学习在艺术创作中的创造潜力，发现 AI 生成的艺术作品与现代艺术概念相关，同时通过色彩构成和主题熟悉度来推测艺术品的喜好和情感体验。