Name
Title | ||
|---|---|---|
Posterior Promoted GAN with Distribution Discriminator for Unsupervised Image Synthesis |
Abstract | ||
|---|---|---|
Sufficient real information in generator is a critical point for the generation ability of GAN. However, GAN and its variants suffer from lack of this point, resulting in brittle training processes. In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P(2)GAN), which promotes generator with the real information in the posterior distribution produced by discriminator. In our framework, different from other variants of GAN, the discriminator maps images to a multivariate Gaussian distribution and extracts real information. The generator employs the real information by AdaIN and a latent code regularizer. Besides, reparameterization trick and pretraining is applied to guarantee a stable training process in practice. The convergence of P(2)GAN is theoretically proved. Experimental results on typical high-dimensional multi-modal datasets demonstrate that P(2)GAN has achieved comparable results with the state-of-the-art variants of GAN on unsupervised image synthesis. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CVPR46437.2021.00645 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 |
DocType | ISSN | Citations |
Conference | 1063-6919 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianchao Zhang | 1 | 313 | 39.57 |
| Ziyang Cheng | 2 | 40 | 7.68 |
| Xiaotong Zhang | 3 | 32 | 4.88 |
| Han Liu | 4 | 40 | 6.96 |