Methods › General › Neural Architecture Search › SCARLET-NAS
SCARLET-NAS
Introduced by Xiangxiang Chu et al. in SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
SCARLET-NAS is a type of neural architecture search that utilises a learnable stabilizer to calibrate feature deviation, named the Equivariant Learnable Stabilizer (ELS). Previous one-shot approaches can be limited by fixed-depth search spaces. With SCARLET-NAS, we use the equivariant learnable stabilizer on each skip connection. This can lead to improved convergence, more reliable evaluation, and retained equivalence. The third benefit is deemed most important by the authors for scalability.
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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SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search 16 Aug 2019 · 1 repository · arXiv:1908.06022
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 |
|---|---|
| AutoML | 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
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