MiniGAN: Toward Informative and Uninformative Image Transferring

Abstract

This article proposes a generative adversarial networks (MiniGAN) to tackle both informative and uninformative image transferring. The generator of MiniGAN is based on the structure of StyleGANv2, in which the encoder and style transform block are proposed to extract the high-level feature maps of the source image and capture the latent representation of the target image, respectively. This information guides the generator for the final image generation. The proposed MiniGAN outperforms other models in style transferring while preserving the color information on the informative images. To test the performance of MiniGAN on the uninformative images, a new data set consisting of 10,000 fashion hand drawings is proposed. Extensive experiments and detailed analysis are presented to demonstrate the performance of MiniGAN.