Web: http://arxiv.org/abs/2205.06032

May 13, 2022, 1:10 a.m. | Xintian Wu, Huanyu Wang, Yiming Wu, Xi Li

cs.CV updates on arXiv.org arxiv.org

As an important and challenging problem, few-shot image generation aims at
generating realistic images through training a GAN model given few samples. A
typical solution for few-shot generation is to transfer a well-trained GAN
model from a data-rich source domain to the data-deficient target domain. In
this paper, we propose a novel self-supervised transfer scheme termed D3T-GAN,
addressing the cross-domain GANs transfer in few-shot image generation.
Specifically, we design two individual strategies to transfer knowledge between
generators and discriminators, respectively. …

arxiv cv data gan gans image image generation transfer

More from arxiv.org / cs.CV updates on arXiv.org

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California