GLeaD: Improving GANs with A Generator-Leading Task
Qingyan Bai, Ceyuan Yang, Yinghao Xu, Xihui liu, Yujiu Yang, Yujun Shen
CVPR 2023

Figure: Concept diagram of our proposed generator-leading task (bottom), as complementary to the discriminator-leading task in the original formulation of GANs (upper). D is required to extract representative features that can be adequately decoded by G to reconstruct the input.

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This work aims at improving Generative adversarial network (GAN) with a generator-leading task. 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.

If you find our work helpful for your research, please consider to cite:

@article{bai2022glead,
  title   = {GLeaD: Improving GANs with A Generator-Leading Task},
  author  = {Bai, Qingyan and Yang, Ceyuan and Xu, Yinghao and Liu, Xihui and Yang, Yujiu and Shen, Yujun},
  journal = {arXiv preprint arXiv:2212.03752},
  year    = {2022}
}