GLeaD: Improving GANs with A Generator-Leading Task
Abstract
Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D), where D is asked to differentiate whether an image comes from real data or is produced by G. Under such a formulation, D plays as the rule maker and hence tends to dominate the competition. Towards a fairer game in GANs, we propose a new paradigm for adversarial training, which makes G assign a task to D as well. Specifically, given an image, we expect D to extract representative features that can be adequately decoded by G to reconstruct the input. That way, instead of learning freely, D is urged to align with the view of G for domain classification. Experimental results on various datasets demonstrate the substantial superiority of our approach over the baselines. For instance, we improve the FID of StyleGAN2 from 4.30 to 2.55 on LSUN Bedroom and from 4.04 to 2.82 on LSUN Church. We believe that the pioneering attempt present in this work could inspire the community with better designed generator-leading tasks for GAN improvement.
Cite
@article{arxiv.2212.03752,
title = {GLeaD: Improving GANs with A Generator-Leading Task},
author = {Qingyan Bai and Ceyuan Yang and Yinghao Xu and Xihui Liu and Yujiu Yang and Yujun Shen},
journal= {arXiv preprint arXiv:2212.03752},
year = {2023}
}
Comments
CVPR2023. Project page: https://ezioby.github.io/glead/ Code: https://github.com/EzioBy/glead/