基于条件Wassertein生成对抗网络的图像生成

生成对抗网络(GAN)能够自动生成目标图像,对相似地块的建筑物排布生成具有重要意义.而目前训练模型的过程中存在生成图像精度不高、模式崩溃、模型训练效率太低的问题.针对这些问题,提出了一种面向图像生成的条件Wassertein生成对抗网络(C-WGAN)模型.首先,该模型需要识别真实样本和目标样本之间特征对应关系,然后,...

Image generation based on conditional-Wassertein generative adversarial network

Generative Adversarial Network (GAN) can automatically generate target images, and is of great significance to the generation of building arrangement of similar blocks. However, there are problems in the existing process of model training such as the low accuracy of generated images, the mode collapse, and the too low efficiency of model training. To solve these problems, a Conditional-Wassertein Generative Adversarial Network (C-WGAN) model for image generation was proposed. First, the feature correspondence between the real sample and the target sample was needed to be identified by this model, and then the target sample was generated according to the identified feature correspondence. The Wassertein distance was used to measure the distance between the distributions of two image features in the model, the GAN training environment was stablized, and mode collapse was avoided during model training, so as to improve the accuracy of the generated images and the training efficiency. Experimental results show that compared with the original Conditional Generative Adversarial Network (CGAN) and the pix2pix models, the proposed model has the Peak Signal-to-Noise Ratio (PSNR) increased by 6.82% and 2.19% at most respectively; in the case of the same number of training rounds, the proposed model reaches the convergence state faster. It can be seen that the proposed model can not only effectively improve the accuracy of image generation, but also increase the convergence speed of the network.