FE-GAN: feature enhancement and fine-grained interaction for text-to-image synthesis
A GAN-based method for generating images from text by introducing multi-kernel convolution into the generator and replacing standard convolutions with multi-layer nested convolutions combined with dynamic gating weighting is proposed, thereby improving the fine-grained quality of generated images.