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

该论文综述了一种使用卷积神经网络（CNN）进行风格转换的新技术，通过利用 CNN 学习的深层图像表征，实现了分离和操作图像内容和风格的能力，从而合成了以和谐方式结合内容和风格的高质量图像。