Methods › Computer Vision › Convolutional Neural Networks › ZFNet

ZFNet

6 papers tagged archive 2025-07-28

Introduced by Matthew D. Zeiler et al. in Visualizing and Understanding Convolutional Networks

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

ZFNet is a classic convolutional neural network. The design was motivated by visualizing intermediate feature layers and the operation of the classifier. Compared to AlexNet, the filter sizes are reduced and the stride of the convolutions are reduced.

PaperSourceSee Code · osmr/imgclsmob

Papers archive 2025-07-28

6 shown of 6, 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

13 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
General Classification3
Image Classification2
Active Learning1
COVID-19 Diagnosis1
Deep Learning1
Object Detection1
Object Recognition1
Sensitivity1
Tensor Networks1
Transfer Learning1
Video Quality Assessment1
image-classification1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with ZFNet: 2013 to 2023, peak 1 1 0 2013: 1 paper 2013 2014: 1 paper 2014 2015: 0 papers 2015 2016: 0 papers 2016 2017: 1 paper 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 1 paper 2020 2021: 1 paper 2021 2022: 0 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (6 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

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