You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Figure:Image editing results using LowRankGAN on StyleGAN2 (first three columns) and BigGAN (last column).
Low-Rank Subspaces in GANs
Jiapeng Zhu, Ruili Feng, Yujun Shen, Deli Zhao, Zhengjun Zha, Jingren Zhou, Qifeng Chen Conference on Neural Information Processing Systems (NeurIPS)
In the repository, we propose LowRankGAN to locally control the image synthesis from GANs with the novel low-rank subspaces. Concretely, we first relate the image regions with the latent space with the help of Jacobian. We then perform low-rank factorization on the Jacobian to get the principal and null spaces. We finally project the principal space w.r.t. the region of interest onto the null space w.r.t. the rest region. In this way, by altering the latent codes along the directions within the projected space, which we call low-rank subspaces, we manage to precisely control the region of interest yet barely affect the rest region.
@inproceedings{zhu2021lowrankgan,
title = {Low-Rank Subspaces in {GAN}s},
author = {Zhu, Jiapeng and Feng, Ruili and Shen, Yujun and Zhao, Deli and Zha, Zhengjun and Zhou, Jingren and Chen, Qifeng},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2021}
}