Methods › General › Neural Architecture Search › DenseNAS
DenseNAS
Introduced by Jiemin Fang et al. in Densely Connected Search Space for More Flexible Neural Architecture Search
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
DenseNAS is a neural architecture search method that utilises a densely connected search space. The search space is represented as a dense super network, which is built upon designed routing blocks. In the super network, routing blocks are densely connected and we search for the best path between them to derive the final architecture. A chained cost estimation algorithm is used to approximate the model cost during the search.
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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Densely Connected Search Space for More Flexible Neural Architecture Search 23 Jun 2019 · 1 repository · arXiv:1906.09607Syntology ran 2 of 2 samples · 0 unverified
Tasks archive 2025-07-28
2 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 |
|---|---|
| Image Classification | 1 |
| Neural Architecture Search | 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