A Brief Review on Cycle Generative Adversarial Networks
Miao Zhu, Shengrong Gong, Zhenjiang Qian, Lifeng Zhang · 2019
Image-to-image translation is an important topic in the field of computer vision. It aims to learn the mapping between input image and output image by training the datasets, and finally translates the image style from one domain to another. In terms of the form of translation, it can be divided into the translation between two domains and multiple domains from different datasets. And it is also divided into pairs and unpaired by the training datasets. As a successful representation of the translation of an unpaired image between two domains, the CycleGAN model is of great significance to the research and application. Starting from the application background of the CycleGAN model, this paper compares it with the basic deep learning model GAN, the paired image translation model pix2pix and other unpaired image-to-image translation models separately, and gives their own model structure diagram. Then, the further application of the CycleGAN in other fields of computer vision is analyzed, especially in the field of Person re-recognition and face change. Finally, according to the actual running results of each model, the existing problems are analyzed and summarized. The further research work is given.