Papers › Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator
Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator
Rajiv Kumar, Rishabh Dabral, G. Sivakumar
Unsupervised image-to-image translation is used to transform images from a source domain to generate images in a target domain without using source-target image pairs. Promising results have been obtained for this problem in an adversarial setting using two independent GANs and attention mechanisms. We propose a new method that uses a single shared discriminator between the two GANs, which improves the overall efficacy. We assess the qualitative and quantitative results on image transfiguration, a cross-domain translation task, in a setting where the target domain shares similar semantics to the source domain. Our results indicate that even without adding attention mechanisms, our method performs at par with attention-based methods and generates images of comparable quality.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image-to-Image Translation | Apples and Oranges | Shared discriminator GAN | Kernel Inception Distance | 4.4 | #1 of 1 | Archive leaderboard | report |
| Image-to-Image Translation | Zebra and Horses | Shared discriminator GAN | Kernel Inception Distance | 5.8 | #1 of 1 | Archive leaderboard | report |
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