基于图像蒙板的无监督图像风格迁移

目前大多数的图像风格迁移方法属于有监督学习，训练数据需要成对出现，并且在处理图像背景时，现有的方法过于繁琐。针对这些问题，提出了一种基于图像蒙板的无监督图像风格迁移方法。在实验中，采用了基于循环一致性的CycleGAN架构，并使用Inception-ResNet结构设计了一个全新的具有内置图像蒙板的生成式模型，最后通过无监督学习将图像的背景与学习到的抽象特征进行自动重组。实验表明，新方法有效地对图像背景和抽象特征进行自动分离与重组，同时解决了特征学习过程中的区域干扰问题，获得了可观的视觉效果。

Unsupervised image style transfer based on image mask

At present, most of the current image style transfer methods are supervised learning, the training data need to appear in pairs. And when the background of the image needs to be processed, the existing methods are too cumbersome. In order to solve these problems, this paper proposed a new method of unsupervised image style transfer based on image mask. In the experiment, it adopted the architecture of the cycle-consistent generative adversarial network, and used the Inception-ResNet structure as the basic component to design a new generative model with the built-in image mask. At last, it automatically reconstructed the background of the image and the learned abstract features through unsupervised learning. Experiments show that the proposed method can effectively separate and reconstruct the background and the learned abstract features of the image, and solves the problem of regional interference in the feature learning process, and achieves considerable visual effects.