  Attribution editing has achieved remarkable progress in recent years owing to
the encoder-decoder structure and generative adversarial network (GAN).
However, it remains challenging in generating 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.
