中圖分類號(hào)： TP391 文獻(xiàn)標(biāo)識(shí)碼： A DOI： 10.19358/j.issn.2096-5133.2021.06.009 引用格式： 胡濤,，李金龍. 基于單階段GANs的文本生成圖像模型[J].信息技術(shù)與網(wǎng)絡(luò)安全,，2021,，40(6)：50-55.
Text to image generation based on single-stage GANs
Hu Tao1，Li Jinlong2
(1.School of Data Science,，University of Science and Technology of China,，Hefei 230026，China,； 2.School of Computer Science and Technology,，University of Science and Technology of China，Hefei 230026,，China)
Abstract： For the current generation of images conditioned on text usually encounters the problems of poor quality and unstable training, a model for generating high-quality images through single-stage generative adversarial networks (GANs) is proposed. Specifically, the attention mechanism is introduced into the generator to generate fine-grained images, also, local language is added to the discriminator to indicate accurate discrimination between the generated image and the real image. Finally, a high-quality image is generated through the mutual game of the generator and the discriminator. The experimental results on the benchmark dataset show that, compared with the latest model with a multi-stage framework, the image generated by the model is more realistic and achieves the highest IS value, which can be better applied to scenes that generate images through text descriptions.
Key words : text to image generation,；generative adversarial networks；attention mechanism
