Methods › Computer Vision › Unpaired Image-to-Image Translation › ALDA
ALDA
Introduced by Minghao Chen et al. in Adversarial-Learned Loss for Domain Adaptation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Adversarial-Learned Loss for Domain Adaptation is a method for domain adaptation that combines adversarial learning with self-training. Specifically, the domain discriminator has to produce different corrected labels for different domains, while the feature generator aims to confuse the domain discriminator. The adversarial process finally leads to a proper confusion matrix on the target domain. In this way, ALDA takes the strengths of domain-adversarial learning and self-training based methods.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Adversarial Branch Architecture Search for Unsupervised Domain Adaptation 12 Feb 2021 · 1 repository · arXiv:2102.06679
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Adversarial-Learned Loss for Domain Adaptation 4 Jan 2020 · 1 repository · arXiv:2001.01046Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Domain Adaptation | 2 |
| Model Selection | 1 |
| Neural Architecture Search | 1 |
| Pseudo Label | 1 |
| Unsupervised Domain Adaptation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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