Abstract: In recent years,more and more generative adversarial networks appear in various fields of deep learning.Conditional generative adversarial networks(cGAN) are the first to introduce supervised learning into unsupervised GANs,which makes it possible for adversarial networks to generate labeled data.Traditional GAN generates images through multiple convolution operations to simulate the dependency among different regions.However,cGAN only improves the objective function of GAN,but does not change its network structure.Therefore,the problem also exists in cGAN that when the distance between features in thegene-rated image is long,features have relatively less relationship,resulting in unclear details of the generated image.In order to solve this problem,this paper introduces Self-attention mechanism to cGAN and proposes a new model named SA-cGAN.The model generates consistent objects or scenes by using features in the long distance of the image,so that the generative ability of conditional GAN is improved.SA-cGAN is experimented on the CelebA and MNIST handwritten datasets and compared with several commonly used generative models such as DCGAN,cGAN.Results prove that the proposed model has made some progress in the field of image generation.

Key words: cGAN, Deep learning, Generative adversarial network, SA-cGAN, Self-attention