Methods › General › Neural Architecture Search › GreedyNAS
GreedyNAS
Introduced by Shan You et al. in GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
GreedyNAS is a one-shot neural architecture search method. Previous methods held the assumption that a supernet should give a reasonable ranking over all paths. They thus treat all paths equally, and spare much effort to train paths. However, it is harsh for a single supernet to evaluate accurately on such a huge-scale search space (eg, 7²¹). GreedyNAS eases the burden of supernet by encouraging focus more on evaluation of potentially-good candidates, which are identified using a surrogate portion of validation data.
Concretely, during training, GreedyNAS utilizes a multi-path sampling strategy with rejection, and greedily filters the weak paths. The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentially-good ones. An exploration and exploitation policy is adopted by introducing an empirical candidate path pool.
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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GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet 25 Mar 2020 · 0 repositories · arXiv:2003.11236
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 |
|---|---|
| All | 1 |
| 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
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