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Facial attributes edit can be seen as an image-to-image translation problem, whose goal is to transfer images from the source domain to the target domain. Specially, facial attributes edit aims at changing some semantic attributes of a given face image while keeping the contents of unrelated area unchanged. The great challenge for this problem lies on the lacking of paired data, i.e. we do not have paired face images that only differ on particular attributes. Moreover, to train a good attributes editing model, there always needs a great amount of train data which labeled by hand. If the train data amount was reduced, then the editing performance would decrease accordingly. Strong intelligent systems should be able to learn knowledge from less data samples (similar idea with few-shot learning). To mitigate this limitation, in this paper, we proposed a Siamese-Network based residual attributes learning model to learn the attributes difference in the high-level latent space. Compared to existing models that perform attributes editing based on an attributes classifier, the proposed deep residual attributes learning model utilized relatively weaker information of attribute differences for face image translation. Sufficient qualitative and quantitative experiments conducted on CelebA dataset proved the effectiveness of our proposed method, moreover, we also adopt the proposed residual attributes learning model in two state-of-the-art models under different data usage percentage to show the effectiveness of the proposed model on boosting attribute editing performance under limited data usage. The experiment results proved that the proposed method can improve data utilization efficiency and thus can boost the editing performance when the train data was limited.

ResAttr-GAN: Unpaired Deep Residual Attributes Learning for Multi-Domain Face Image Translation