Papers › DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training
DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training
Jiaheng Wei, Minghao Liu, Jiahao Luo, Andrew Zhu, James Davis, Yang Liu
In this paper, we introduce DuelGAN, a generative adversarial network (GAN) solution to improve the stability of the generated samples and to mitigate mode collapse. Built upon the Vanilla GAN's two-player game between the discriminator D₁ and the generator G, we introduce a peer discriminator D₂ to the min-max game. Similar to previous work using two discriminators, the first role of both D₁, D₂ is to distinguish between generated samples and real ones, while the generator tries to generate high-quality samples which are able to fool both discriminators. Different from existing methods, we introduce another game between D₁ and D₂ to discourage their agreement and therefore increase the level of diversity of the generated samples. This property alleviates the issue of early mode collapse by preventing D₁ and D₂ from converging too fast. We provide theoretical analysis for the equilibrium of the min-max game formed among G, D₁, D₂. We offer convergence behavior of DuelGAN as well as stability of the min-max game. It's worth mentioning that DuelGAN operates in the unsupervised setting, and the duel between D₁ and D₂ does not need any label supervision. Experiments results on a synthetic dataset and on real-world image datasets (MNIST, Fashion MNIST, CIFAR-10, STL-10, CelebA, VGG, and FFHQ) demonstrate that DuelGAN outperforms competitive baseline work in generating diverse and high-quality samples, while only introduces negligible computation cost.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | CIFAR-10 | PeerGAN | FID | 21.55 | #56 of 78 | Archive leaderboard | report |
| Image Generation | CelebA 64x64 | PeerGAN | FID | 13.95 | #30 of 39 | Archive leaderboard | report |
| Image Generation | Fashion-MNIST | PeerGAN | FID | 21.73 | #3 of 7 | Archive leaderboard | report |
| Image Generation | STL-10 | PeerGAN | FID | 51.37 | #29 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections