AI艺术神经星座：揭示AI生成艺术与人类艺术的集体与对比状态
AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art
AI总结：
本研究通过构建ArtConstellation数据集和深度CNN分析，对比AI生成艺术与人类艺术，发现AI艺术与现代艺术原则相关，且在CLIP空间中具有分布内和分布外特征，并提出了ArtNeuralConstellation分析框架。
摘要：
发现随机信号在美学和概念丰富性方面对各种艺术表现形式的创造性潜力，是生成式机器学习作为艺术创作方式近期成功的基础。为了更好地理解这种新的艺术媒介，我们进行了一项全面分析，将AI生成的艺术定位在人类艺术遗产的背景下。我们的比较分析基于一个名为“ArtConstellation”的大型数据集，该数据集包含6,000幅WikiArt作品和3,200幅AI生成艺术作品的关于艺术原则、喜爱度和情感的标注。在训练了各种最先进的生成模型后，我们生成了艺术样本，并在用于风格分类的深度CNN的最后一个隐藏层上与WikiArt数据进行比较。我们积极研究了各种艺术原则以解释神经表示，并利用它们来驱动关于人类和AI生成艺术的比较知识。语义分析中的一个关键发现是，AI生成的艺术作品在视觉上与1800-2000年现代时期艺术的原则概念相关。此外，通过在CLIP空间中的分布外（OOD）和分布内（ID）检测，我们发现当AI生成的艺术描绘风景和几何抽象图形时，它们与人类艺术属于ID，而当机器艺术由变形和扭曲的图形组成时，则被检测为OOD。我们观察到机器生成的艺术以不完整和简化的形象为独特特征。最后，我们进行了一项关于情感体验的人类调查。色彩构成和熟悉的主题是艺术欣赏中喜爱度和情感的关键因素。我们提出我们的整体方法和收集的数据集作为对比人类和AI生成艺术的分析框架，我们称之为“ArtNeuralConstellation”。代码可在以下网址获取：https://github.com/faixan-khan/ArtNeuralConstellation
英文摘要：
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: https://github.com/faixan-khan/ArtNeuralConstellation
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
