A novel cross channel self-attention based approach for facial attribute editing - Xu, Meng; Jin, Rize; Lu, Liangfu; Chung, Tae Sun (KSII Transactions on Internet and Information Systems, 2021)
Although significant progress has been made in synthesizing visually realistic face images by Generative Adversarial Networks (GANs), there still lacks effective approaches to provide fine-grained control over the generation process for semantic facial attribute editing. In this work, we propose a novel cross channel self-attention based generative adversarial network (CCA-GAN), which weights the importance of multiple channels of features and archives pixel-level feature alignment and conversion, to reduce the impact on irrelevant attributes while editing the target attributes. Evaluation results show that CCA-GAN outperforms state-of-the-art models on the CelebA dataset, reducing Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) by 15∼28% and 25∼100%, respectively. Furthermore, visualization of generated samples confirms the effect of disentanglement of the proposed model.
Keywords: Adversarial networks; Effective approaches; Evaluation results; Feature alignment; Fine-grained control; Generation process; Multiple channels; State of the art; Cross channel self-attention; Facial attribute editing; Generative adversarial network; Image translation; Style transfer