Methods › Computer Vision › Convolutional Neural Networks › OverFeat

OverFeat

7 papers tagged archive 2025-07-28

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

OverFeat is a classic type of convolutional neural network architecture, employing convolution, pooling and fully connected layers. The Figure to the right shows the architectural details.

Source: OverFeat: Integrated Recognition, Localization and...See Code · tensorflow/models

Papers archive 2025-07-28

7 shown of 7, 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.

DyVEDeep: Dynamic Variable Effort Deep Neural Networks
4 Apr 2017 · 0 repositories · arXiv:1704.01137
Fast Training of Convolutional Neural Networks via Kernel Rescaling
12 Oct 2016 · 0 repositories · arXiv:1610.03623
Bag of Attributes for Video Event Retrieval
18 Jul 2016 · 0 repositories · arXiv:1607.05208
vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design
25 Feb 2016 · 4 repositories · arXiv:1602.08124
Where Is My Puppy? Retrieving Lost Dogs by Facial Features
9 Oct 2015 · 0 repositories · arXiv:1510.02781
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
18 Jun 2014 · 14 repositories · arXiv:1406.4729

Syntology ran 0 of 1 samples · 1 unverified
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
21 Dec 2013 · 4 repositories · arXiv:1312.6229

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.

Task	Papers
General Classification	2
Image Classification	2
Object Detection	2
Object Recognition	2
Attribute	1
BIG-bench Machine Learning	1
CPU	1
Efficient Neural Network	1
Face Recognition	1
GPU	1
Retrieval	1
image-classification	1
object-detection	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (7 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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