ClsGAN: Selective Attribute Editing Model Based On Classification Adversarial Network

TL;DR

ClsGAN is a novel model that improves attribute editing in images by balancing transfer accuracy and photo-realism using a classification adversarial network and specialized modules.

Contribution

The paper introduces Tr-resnet and Atta-cls modules that enhance attribute transfer accuracy and image quality in a new selective editing framework based on GAN.

Findings

Outperforms state-of-the-art in CelebA attribute editing

Achieves high-quality, accurate attribute transfer

Demonstrates effectiveness of proposed modules through ablation studies

Abstract

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.