Papers › Unsupervised Cross-Domain Image Generation
Unsupervised Cross-Domain Image Generation
Yaniv Taigman, Adam Polyak, Lior Wolf
We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity.
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Code
Syntology Ran 2 of 18 code samples harvested from 3 repositories linked to this paper; 16 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong.
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Code Syntology ran Syntology
18 samples harvested; 2 ran; 1 honoured the contract we drafted; 16 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Image-To-Image Translation | SVNH-to-MNIST | DTN | Classification Accuracy | 84.4% | #2 of 4 | 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.
Methods
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