Papers › Coupled Generative Adversarial Networks
Coupled Generative Adversarial Networks
Ming-Yu Liu, Oncel Tuzel
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images. It can learn a joint distribution with just samples drawn from the marginal distributions. This is achieved by enforcing a weight-sharing constraint that limits the network capacity and favors a joint distribution solution over a product of marginal distributions one. We apply CoGAN to several joint distribution learning tasks, including learning a joint distribution of color and depth images, and learning a joint distribution of face images with different attributes. For each task it successfully learns the joint distribution without any tuple of corresponding images. We also demonstrate its applications to domain adaptation and image transformation.
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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-to-Image Translation | Cityscapes Labels-to-Photo | CoGAN | Class IOU | 0.06 | #18 of 21 | Archive leaderboard | report |
| Image-to-Image Translation | Cityscapes Labels-to-Photo | CoGAN | Per-class Accuracy | 10% | #18 of 21 | Archive leaderboard | report |
| Image-to-Image Translation | Cityscapes Labels-to-Photo | CoGAN | Per-pixel Accuracy | 40% | #18 of 21 | Archive leaderboard | report |
| Image-to-Image Translation | Cityscapes Photo-to-Labels | CoGAN | Class IOU | 0.08 | #3 of 5 | Archive leaderboard | report |
| Image-to-Image Translation | Cityscapes Photo-to-Labels | CoGAN | Per-class Accuracy | 11% | #3 of 5 | Archive leaderboard | report |
| Image-to-Image Translation | Cityscapes Photo-to-Labels | CoGAN | Per-pixel Accuracy | 45% | #3 of 5 | 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.
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