Chunwei Tian1,2,3,4, Haoyang Gao2,3, Pengwei Wang2, Bob Zhang1,*
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 105-118, 2024, DOI:10.32604/cmc.2024.052097
Abstract Generative adversarial networks (GANs) with gaming abilities have been widely applied in image generation. However, gamistic generators and discriminators may reduce the robustness of the obtained GANs in image generation under varying scenes. Enhancing the relation of hierarchical information in a generation network and enlarging differences of different network architectures can facilitate more structural information to improve the generation effect for image generation. In this paper, we propose an enhanced GAN via improving a generator for image generation (EIGGAN). EIGGAN applies a spatial attention to a generator to extract salient information to enhance the truthfulness… More >
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