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：艺术风格迁移作为生成式人工智能的一项引人入胜的应用，涉及将一幅图像的内容与另一幅图像的艺术风格相融合，以创作出独特的视觉作品。本文全面概述了一种利用卷积神经网络（CNN）进行风格迁移的新颖技术。通过利用CNN学习到的深度图像表征，我们演示了如何分离并操控图像的内容与风格，从而合成出能够和谐融合内容与风格的高质量图像。我们详细描述了该方法，包括内容与风格的表征、损失函数的计算及优化过程，并通过实验结果展示了该方法在不同风格与内容下的有效性及多样性。