Methods › General › Domain Adaptation › DistanceNet
DistanceNet
Introduced by Han Guo et al. in Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
DistanceNet is a learning algorithm for multi-source domain adaptation that uses various distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation.
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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Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits 13 Jan 2020 · 0 repositories · arXiv:2001.04362
Tasks archive 2025-07-28
7 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 |
|---|---|
| Classification | 1 |
| Domain Adaptation | 1 |
| General Classification | 1 |
| Sentiment Analysis | 1 |
| Text Classification | 1 |
| Unsupervised Domain Adaptation | 1 |
| text-classification | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections