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

由于编解码器结构和生成对抗网络（GAN），归因编辑近年来取得了显着进展。但是，要生成具有精确属性转换的高质量图像仍然很困难。针对这些问题，该工作提出了一种基于分类对抗网络（称为ClsGAN）的新颖的选择性属性编辑模型，该模型显示了属性传递精度和逼真的图像之间的良好平衡。考虑到编解码器结构中的跳过连接，使编辑图像容易受到原始属性的影响，因此提出了一种上层卷积残差网络（称为Tr-resnet），以从源图像和目标标签中选择性地提取信息。 。另外，为了进一步提高生成图像的传输精度，引入属性对抗分类器（称为Atta-cls），通过学习属性传递图像的缺陷，从属性的角度指导生成器。CelebA上的实验结果表明，我们的ClsGAN在图像质量和传输精度方面优于最新方法。此外，消融研究还旨在验证Tr-resnet和Atta-cls的出色性能。