Methods › General › Domain Adaptation › L2M
Learning to Match
L2M
Introduced by Chaohui Yu et al. in Learning to Match Distributions for Domain Adaptation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
L2M is a learning algorithm that can work for most cross-domain distribution matching tasks. It automatically learns the cross-domain distribution matching without relying on hand-crafted priors on the matching loss. Instead, L2M reduces the inductive bias by using a meta-network to learn the distribution matching loss in a data-driven way.
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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Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning 26 Jul 2022 · 1 repository · arXiv:2207.12831
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Learning to Match Distributions for Domain Adaptation 17 Jul 2020 · 1 repository · arXiv:2007.10791
Tasks archive 2025-07-28
3 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 |
|---|---|
| BIG-bench Machine Learning | 1 |
| Domain Adaptation | 1 |
| Inductive Bias | 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