Methods › Computer Vision › Convolutional Neural Networks › SCARLET

SCARLET

4 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

SCARLET is a type of convolutional neural architecture learnt by the SCARLET-NAS neural architecture search method. The three variants are SCARLET-A, SCARLET-B and SCARLET-C. The basic building block is MBConvs from MobileNetV2. Squeeze-and-excitation layers are also experimented with.

Source: SCARLET-NAS: Bridging the Gap between Stability and...See Code · xiaomi-automl/SCARLET-NAS

Papers archive 2025-07-28

4 shown of 4, 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.

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.

TaskPapers
AutoML1
Cultural Vocal Bursts Intensity Prediction1
Federated Learning1
Image Classification1
Management1
Neural Architecture Search1
Time Series1

Usage over time archive 2025-07-28

Papers per year tagged with SCARLET: 2019 to 2025, peak 1 1 0 2019: 1 paper 2019 2020: 0 papers 2020 2021: 0 papers 2021 2022: 1 paper 2022 2023: 1 paper 2023 2024: 0 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

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

Convolutional Neural Networks

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