Image semantic segmentation has always been one of the most challenging tasks in computer vision. At present

most semantic segmentation algorithms based on convolutional neural networks have the problem of inaccuracy and discontinuity of segmentation results. In order to improve image segmentation effect

propose image semantic segmentation network model based on generative adversarial learning

which consists of generating network and discriminating network. The generative network contains five modules

and the main function is to generate semantic segmentation image. The main role of the discriminative network is to confront with the generative network to make the generated semantic segmentation image closer to Ground truth. The semantic segmentation results on the Pascal VOC 2012 show that the proposed algorithm can effectively improve precision of image semantic segmentation.