English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Face image translation has made notable progress in recent years. However,when training on limited data, the performance of existing approachessignificantly declines. Although some studies have attempted to tackle thisproblem, they either failed to achieve the few-shot setting (less than 10) orcan only get suboptimal results. In this paper, we propose GAN PriorDistillation (GPD) to enable effective few-shot face image translation. GPDcontains two models: a teacher network with GAN Prior and a student networkthat fulfills end-to-end translation. Specifically, we adapt the teachernetwork trained on large-scale data in the source domain to the target domainwith only a few samples, where it can learn the target domain's knowledge.Then, we can achieve few-shot augmentation by generating source domain andtarget domain images simultaneously with the same latent codes. We propose ananchor-based knowledge distillation module that can fully use the differencebetween the training and the augmented data to distill the knowledge of theteacher network into the student network. The trained student network achievesexcellent generalization performance with the absorption of additionalknowledge. Qualitative and quantitative experiments demonstrate that our methodachieves superior results than state-of-the-art approaches in a few-shotsetting.

Few-shot Face Image Translation via GAN Prior Distillation