The multiple fusion modules of deep fusion generative adversarial network(DF-GAN)were independent of each other,which leaded to a shallow fusion depth and made it difficult to obtain the optimal fusion result.Hence,a text-to-im-age synthesis algorithm which based on deep propagated fusion generative adversarial network(DPF-GAN)was proposed to solve these issues.This algorithm connected adjacent affine and fusion modules through concatenation,so that the previous fu-sion information can be propagated to the subsequent fusion modules.This facilitates a deeper integration of text and image.Through experimental results on the CUB-200-2011 dataset and COCO dataset,found that the quality of images which gener-ated by DPF-GAN was better than DF-GAN.The FID score on CUB-200-2011 dataset was decreased by approximately 11.34%compared to DF-GAN.Compared to the Recurrent affine transformation generative adversarial network(RAT-GAN),DPF-GAN offers lower spatial complexity and faster inference speed.
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