基于生成对抗神经网络的图像风格快速迁移Fast Image Style Transfer Based on Generative Adversarial Networks摘要现今，无论是科研还是娱乐，人们对于图片和视频的处理需求越来越大，所以需要更多的处理图片和视频的应用，比如图像风格迁移。对于图像风格迁移这个领域，人类很难快速学习到某种图片风格，而且利用人力在专门软件上处理图片是很耗时间的，所以人们需要可以自动化的快速的迁移方法。本文主要是实现了一个可以对图像风格进行快速风格迁移的应用。针对传统图像迁移方法迁移速度慢，复用性弱的缺点，这里使用了一种更新型的神经网络方法——循环一致性生成对抗网络（Cycle-Consistent Generative Adversarial Networks, CycleGAN）。编写代码实现了 CycleGAN 中的鉴别器和生成器之后，通过神经网络的迭代训练，可以训练出用来进行图像风格迁移的生成器。利用这项技术，本文训练出了 7 个风格迁移生成器，分别有：梵高，莫奈，塞尚，浮世绘，齐白石，唐寅，山水画。除了实现照片风格迁移，本文还实现了两个相似物体之间的风格互换。一共 3 种：马与斑马互转，夏季与冬季互转，苹果与橘子互转。I关键词： 图像风格迁移；快速迁移；CycleGAN；生成对抗网络；鉴别器；生成器AbstractNowadays, whether it is for scientific research or entertainment, people have an increasing demand for image and video processing, so more and IImore applications of image and video processing are needed, such as image style transfer. In the field of image style transfer, it is difficult for human beings to learn a certain image style quickly, and it takes time to process images on specialized software with human beings, so people need a fast method of migration that can be automated.This paper mainly realizes an application of fast style migration of image style. Image migration method for traditional migration speed slow, problem of weak reusability here USES a new type of neural network method more consistency - loop generated against network (Cycle - Consistent Generative Adversarial Networks, CycleGAN). After coding t...
