GAN Network Solves The Problem of Image Segmentation Across Data

Since FCN [1] added deep neural network to image segmentation technology, the image segmentation effect has been significantly improved in recent years. For example, the VOC2012 data set has been improved from 40% to more than 80%, and the effect has doubled. . However, the improvement of the effect is only for the training set and the verification set of the same data set. When the crossdata set is verified, the accuracy rate will drop sharply. In response to this problem, we propose to apply the CycleGAN [2] network to image segmentation to learn the style features between different datasets. It turns out that the processing can improve the effect of image segmentation between different datasets.