Methods › Computer Vision › Convolutional Neural Networks › GENet
GPU-Efficient Network
GENet
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
GENets, or GPU-Efficient Networks, are a family of efficient models found through neural architecture search. The search occurs over several types of convolutional block, which include depth-wise convolutions, batch normalization, ReLU, and an inverted bottleneck structure.
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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Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNet 1 Jul 2020 · 1 repository · arXiv:2007.00649
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Neural Architecture Design for GPU-Efficient Networks 24 Jun 2020 · 2 repositories · arXiv:2006.14090Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
Tasks archive 2025-07-28
6 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 |
|---|---|
| Action Recognition | 1 |
| Ensemble Learning | 1 |
| GPU | 1 |
| Neural Architecture Search | 1 |
| Object Detection | 1 |
| object-detection | 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