MRP-GAN: Multi-resolution Parallel Generative Adversarial Networks for Text-to-Image Synthesis

Abstract Synthesizing photographic images from given text descriptions is a challenging problem. Although current methods first synthesize an initial blurred image, then refine the initial image to a high-quality one, the most existing methods are difficult to refine the initial image to an image corresponding to the text description. In this paper, the Multi-resolution Parallel Generative Adversarial Networks for Text-to-Image Synthesis (MRP-GAN) is proposed to generate photographic images. MRP-GAN introduces a Multi-resolution Parallel structure to refine the initial images when the initial images are not synthesized well. The low-resolution semantics are maintained through the whole process by Multi-resolution Parallel structure. Response Gate is designed to fully explore the capability of Multi-resolution Parallel structure by aggregating the outputs of the multi-resolution parallel subnetworks. We also utilize an attention mechanism, named Residual Attention Network, to fine-tune more fine-grained details of the generated images. We evaluate our MRP-GAN model on the CUB and MS-COCO datasets. Extensive experiments demonstrate the state-of-the-art performance of MRP-GAN. Besides, we apply a Multi-resolution Parallel structure in the existing method to verify its transferability.