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Attribution editing has achieved remarkable progress in recent years owing tothe encoder-decoder structure and generative adversarial network (GAN).However, it remains challenging in generating high-quality images with accurateattribute transformation. Attacking these problems, the work proposes a novelselective attribute editing model based on classification adversarial network(referred to as ClsGAN) that shows good balance between attribute transferaccuracy and photo-realistic images. Considering that the editing images areprone to be affected by original attribute due to skip-connection inencoder-decoder structure, an upper convolution residual network (referred toas Tr-resnet) is presented to selectively extract information from the sourceimage and target label. In addition, to further improve the transfer accuracyof generated images, an attribute adversarial classifier (referred to asAtta-cls) is introduced to guide the generator from the perspective ofattribute through learning the defects of attribute transfer images.Experimental results on CelebA demonstrate that our ClsGAN performs favorablyagainst state-of-the-art approaches in image quality and transfer accuracy.Moreover, ablation studies are also designed to verify the great performance ofTr-resnet and Atta-cls.

ClsGAN: Selective Attribute Editing Model Based On Classification Adversarial Network