Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image representations learned by CNNs, we demonstrate how to separate and manipulate image content and style, enabling the synthesis of high-quality images that combine content and style in a harmonious manner. We describe the methodology, including content and style representations, loss computation, and optimization, and showcase experimental results highlighting the effectiveness and versatility of the approach across different styles and content

翻译：艺术风格迁移是生成式人工智能中一个引人入胜的应用，它涉及将一幅图像的内容与另一幅图像的艺术风格相融合，以创造出独特的视觉构成。本文全面概述了一种基于卷积神经网络（CNN）的风格迁移新技术。通过利用CNN学习到的深度图像表征，我们展示了如何分离并操控图像的内容与风格，从而实现内容与风格和谐融合的高质量图像合成。文中详细阐述了方法论，包括内容与风格的表征、损失计算与优化，并展示了实验结果，突显了该方法在不同风格和内容下的有效性及通用性。