Methods › Computer Vision › Convolutional Neural Networks › SENet

SENet

47 papers tagged archive 2025-07-28

Introduced by Jie Hu et al. in Squeeze-and-Excitation Networks

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

A SENet is a convolutional neural network architecture that employs squeeze-and-excitation blocks to enable the network to perform dynamic channel-wise feature recalibration.

PaperSourceSee Code · osmr/imgclsmob

Papers archive 2025-07-28

30 shown of 47, 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

20 shown of 67 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
Image Classification11
Object Detection7
Semantic Segmentation7
image-classification7
Classification5
object-detection5
Instance Segmentation4
Segmentation4
Deep Learning3
Clustering2
Emotion Recognition2
General Classification2
Representation Learning2
Sensitivity2
Weather Forecasting2
3D Object Detection1
Action Classification1
Action Recognition1
Adversarial Defense1
All-day Semantic Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with SENet: 2017 to 2025, peak 9 9 0 2017: 3 papers 2017 2018: 4 papers 2018 2019: 5 papers 2019 2020: 5 papers 2020 2021: 9 papers 2021 2022: 4 papers 2022 2023: 6 papers 2023 2024: 9 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (47 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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