Methods › General › Domain Adaptation › DAEL
Domain Adaptive Ensemble Learning
DAEL
Introduced by Kaiyang Zhou et al. in Domain Adaptive Ensemble Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Domain Adaptive Ensemble Learning, or DAEL, is an architecture for domain adaptation. The model is composed of a CNN feature extractor shared across domains and multiple classifier heads each trained to specialize in a particular source domain. Each such classifier is an expert to its own domain and a non-expert to others. DAEL aims to learn these experts collaboratively so that when forming an ensemble, they can leverage complementary information from each other to be more effective for an unseen target domain. To this end, each source domain is used in turn as a pseudo-target-domain with its own expert providing supervisory signal to the ensemble of non-experts learned from the other sources. For unlabeled target data under the UDA setting where real expert does not exist, DAEL uses pseudo-label to supervise the ensemble learning.
Papers archive 2025-07-28
1 shown of 1, 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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Domain Adaptive Ensemble Learning 16 Mar 2020 · 1 repository · arXiv:2003.07325
Tasks archive 2025-07-28
6 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 | 1 |
| Domain Generalization | 1 |
| Ensemble Learning | 1 |
| Multi-Source Unsupervised Domain Adaptation | 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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