The works of Gatys et al. demonstrated the capability of Convolutional Neural Networks (CNNs) in creating artistic style images. This process of transferring content images in different styles is called Neural Style Transfer (NST). In this paper, we re-implement image-based NST, fast NST, and arbitrary NST. We also explore to utilize ResNet with activation smoothing in NST. Extensive experimental results demonstrate that smoothing transformation can greatly improve the quality of stylization results.

翻译：Gatys等人的研究展示了卷积神经网络（CNN）在生成艺术风格图像方面的能力。将内容图像转换为不同风格的过程被称为神经风格迁移（NST）。本文重新实现了基于图像的NST、快速NST以及任意风格NST。我们还探索了在NST中结合使用ResNet与激活平滑技术。大量实验结果表明，平滑变换能够显著提升风格化结果的质量。