目的 传统的图像风格迁移主要在两个配对的图像间进行。循环一致性对抗网络（CycleGAN）首次将生成对抗网络应用于图像风格迁移，实现无配对图像之间的风格迁移，取得了一定的效果，但泛化能力较弱，当训练图像与测试图像之间差距较大时，迁移效果不佳。针对上述问题，本文提出了一种结合全卷积网络（FCN）与CycleGAN的图像风格迁移方法，使得图像能够实现特定目标之间的实例风格迁移。同时验证了训练数据集并非是造成CycleGAN风格迁移效果不佳的因素。方法 首先结合全卷积网络对图像进行语义分割，确定风格迁移的目标，然后将风格迁移后的图像与目标进行匹配，确定迁移对象实现局部风格迁移。为验证CycleGAN在训练图像和测试图像差距较大时风格转移效果不佳并非因缺少相应训练集，制作了训练数据集并带入原网络训练。结果 实验表明结合了全卷积网络与CycleGAN的图像风格迁移方法增加了识别能力，能够做到图像局部风格迁移而保持其余元素的完整性，相对于CycleGAN，该方法能够有效抑制目标之外区域的风格迁移，实验中所用4张图片平均只有4.03%的背景像素点发生了改变，实例迁移效果得到很好提升。而将自制训练集带入原网络训练后，依然不能准确地在目标对象之间进行风格迁移。结论 结合了全卷积网络与CycleGAN的方法能够实现图像的局部风格迁移而保持目标对象之外元素不发生改变，而改变训练数据集对CycleGAN进行实例风格迁移准确性的影响并不大。

Objective Gatys et al. successfully used convolutional neural networks (CNNs) to render a content image in different styles in a process referred to as neural style transfer (NST). Their work was the first time deep learning demonstrated its ability in the field of style transfer. In the past, most related problems in style transfer were manually modeled, which was a time consuming and laborious process. The goal of traditional NST is to learn the mapping between two different styles of paired images. Cycle-consistent adversarial networks (CycleGAN) is the first method to apply the generative adversarial network (GAN) to image style transfer. This method has a good performance on unpaired training data but does not work well when the test image is different from the training images. Thus, instance style transfer was developed to address this problem. Instance style transfer is built on image segmentation and should be applied only on the object of interest. The main challenge is the transition between the object and a non-stylized background. Most studies on instance style transfer have focused on the CNN. In this paper, some of these methods are extended to CycleGAN, and some steps are improved based on actual conditions. We propose a method to achieve instance style transfer by combining fully convolutional network (FCN) with CycleGAN. A dataset is used to verify that training data are not the reason CycleGAN cannot work well on instance style transfer. Method This study is divided into two parts:The first part is to improve the performance of CycleGAN to make it work efficiently in instance style transfer. The second part is to verify the conjecture in the reference. In the first part, the FCN is utilized to obtain the semantic segmentation of input image X. FCN must be trained by a large amount of labeled data in advance so that the network can segment the object with high accuracy. The output of the FCN is label image Y. Next, CycleGAN is utilized for style transfer. In this step, CycleGAN must be trained with the prepared data to obtain style transfer image Z. Then, the output of the CycleGAN Z is matched with the label image Y. When referring to image matching, we make the pixel points outside the areas of interests in the FCN's label image Y to be zero and make the Hadamard product R=Y·Z. In this way, the areas of interests can be separated from the style transfer image Z, and R is used to replace the pixel in the same location of the original image X. For the second part, we create training sets for people riding horses, people beside horses, people riding zebras, or people with zebras to verify the problem raised by CycleGAN's author. Then, this dataset is used to train CycleGAN and observe the result. Data augmentation needs to be performed because images are hard to find. Result The first experiment shows that the recognition ability of CycleGAN improves considerably when combined with the FCN. The proposed method can achieve instance style transfer of the image, while the rest of the area in the image has minimal influence. In the experiment, we define an index that calculates the number of pixels changed outside the target object to measure the performance of instance style transfer and show the improvement of this method. A smaller index corresponds to improved performance of the instance style transfer. In the numerical simulations, the value of our method is smaller than that of the style transfer that uses CycleGAN only, thereby showing that the proposed method is more efficient in instance style transfer. In the second experiment, using our dataset to train CycleGAN shows that the CycleGAN is still unable to achieve instance style transfer. The network is difficult to train and oscillation of loss function is violent because of the complex color and background of the new training dataset. However, the instance style transfer performance of using a new dataset to train is improved. Conclusion FCN can obtain the semantic segmentation of an image. CycleGAN combined with FCN can achieve instance style transfer and ensure that the background and other objects remain unchanged. We verify that CycleGAN cannot accurately achieve instance style transfer of a given target when the test image is different from the training images.