Methods › Computer Vision › Convolutional Neural Networks › PyramidNet

PyramidNet

9 papers tagged archive 2025-07-28

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

A PyramidNet is a type of convolutional network where the key idea is to concentrate on the feature map dimension by increasing it gradually instead of by increasing it sharply at each residual unit with downsampling. In addition, the network architecture works as a mixture of both plain and residual networks by using zero-padded identity-mapping shortcut connections when increasing the feature map dimension.

Source: Deep Pyramidal Residual NetworksSee Code · osmr/imgclsmob

Papers archive 2025-07-28

9 shown of 9, 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

10 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 Classification4
General Classification2
image-classification2
Classification1
Decoder1
Language Modeling1
Language Modelling1
Object Recognition1
Stochastic Optimization1
model1

Usage over time archive 2025-07-28

Papers per year tagged with PyramidNet: 2016 to 2023, peak 3 3 0 2016: 2 papers 2016 2017: 2 papers 2017 2018: 3 papers 2018 2019: 0 papers 2019 2020: 1 paper 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (9 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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