Very Deep Convolutional Networks for Large-Scale Image Recognition

E366102

computer vision paper research paper scientific publication

"Very Deep Convolutional Networks for Large-Scale Image Recognition" is the influential 2014 research paper that introduced the VGG family of deep convolutional neural network architectures, demonstrating that significantly increasing network depth with small convolutional filters leads to substantial improvements in image classification performance.

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All labels observed (3)

Label	Occurrences
Very Deep Convolutional Networks for Large-Scale Image Recognition canonical	4
VGG paper	1
VGG-16 and VGG-19 achieve state-of-the-art performance on ImageNet at the time of publication	1

How this entity was disambiguated

This entity first appeared as the object of triple T3520348 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: Very Deep Convolutional Networks for Large-Scale Image Recognition
Context triple: [VGG, describedInPaper, Very Deep Convolutional Networks for Large-Scale Image Recognition]

A. Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
B. ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
C. Large-Scale Distributed Deep Networks
Large-Scale Distributed Deep Networks is a seminal research work that introduced methods for training deep neural networks efficiently across large-scale distributed computing infrastructure, enabling breakthroughs in modern large-scale AI systems.
D. Long-term Recurrent Convolutional Networks for Visual Recognition and Description
"Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
E. Deep Convolutional GAN
Deep Convolutional GAN is a widely used GAN architecture that replaces fully connected layers with deep convolutional layers to generate high-quality, realistic images.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: Very Deep Convolutional Networks for Large-Scale Image Recognition
Target entity description: "Very Deep Convolutional Networks for Large-Scale Image Recognition" is the influential 2014 research paper that introduced the VGG family of deep convolutional neural network architectures, demonstrating that significantly increasing network depth with small convolutional filters leads to substantial improvements in image classification performance.

A. Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
B. ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
C. Large-Scale Distributed Deep Networks
Large-Scale Distributed Deep Networks is a seminal research work that introduced methods for training deep neural networks efficiently across large-scale distributed computing infrastructure, enabling breakthroughs in modern large-scale AI systems.
D. Long-term Recurrent Convolutional Networks for Visual Recognition and Description
"Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
E. Deep Convolutional GAN
Deep Convolutional GAN is a widely used GAN architecture that replaces fully connected layers with deep convolutional layers to generate high-quality, realistic images.
F. None of above. chosen

Statements (46)

Predicate	Object
instanceOf	computer vision paper ⓘ research paper ⓘ scientific publication ⓘ
application	image feature extraction ⓘ object recognition ⓘ transfer learning ⓘ
architectureStyle	sequential convolutional layers ⓘ very deep convolutional neural network ⓘ
benchmark	ImageNet ⓘ surface form: ILSVRC-2014 ImageNet ⓘ
category	convolutional neural networks ⓘ
datasetSize	over one million training images ⓘ
demonstrates	benefits of using small 3x3 filters instead of larger filters ⓘ effectiveness of depth over width in CNN design ⓘ
designPrinciple	homogeneous architecture with similar layer configurations ⓘ use of small convolutional filters stacked to increase receptive field ⓘ
domain	artificial intelligence ⓘ
evaluationMetric	top-1 error ⓘ top-5 error ⓘ
field	computer vision ⓘ deep learning ⓘ machine learning ⓘ
impact	highly influential in the development of deep CNN architectures ⓘ widely adopted as a baseline model in computer vision research ⓘ
influenced	later CNN architectures such as ResNet ⓘ use of VGG-style backbones in many vision models ⓘ
inspired	pretrained CNN feature extraction in many applications ⓘ use of deep feature representations for downstream tasks ⓘ
language	English ⓘ
lossFunction	softmax cross-entropy ⓘ
mainContribution	demonstration that increasing network depth with small convolution filters improves image classification performance ⓘ introduction of the VGG family of deep convolutional neural networks ⓘ
optimizationMethod	stochastic gradient descent ⓘ
proposes	VGG ⓘ surface form: VGG-11 architecture VGG ⓘ surface form: VGG-13 architecture VGG ⓘ surface form: VGG-16 architecture VGG ⓘ surface form: VGG-19 architecture
shortTitle	Very Deep Convolutional Networks for Large-Scale Image Recognition self-linksurface differs ⓘ surface form: VGG paper
shows	Very Deep Convolutional Networks for Large-Scale Image Recognition self-linksurface differs ⓘ surface form: VGG-16 and VGG-19 achieve state-of-the-art performance on ImageNet at the time of publication deeper convolutional networks can achieve better accuracy than shallower ones ⓘ
task	large-scale image classification ⓘ
title	Very Deep Convolutional Networks for Large-Scale Image Recognition self-link ⓘ
uses	1x1 convolutional filters ⓘ 3x3 convolutional filters ⓘ max pooling layers ⓘ
year	2014 ⓘ

How these facts were elicited

Referenced by (6)

Full triples — surface form annotated when it differs from this entity's canonical label.

VGG → describedInPaper → Very Deep Convolutional Networks for Large-Scale Image Recognition ⓘ

Karen Simonyan → notableWork → Very Deep Convolutional Networks for Large-Scale Image Recognition ⓘ

Visual Geometry Group → notableWork → Very Deep Convolutional Networks for Large-Scale Image Recognition ⓘ

Very Deep Convolutional Networks for Large-Scale Image Recognition → title → Very Deep Convolutional Networks for Large-Scale Image Recognition self-link ⓘ

Very Deep Convolutional Networks for Large-Scale Image Recognition → shortTitle → Very Deep Convolutional Networks for Large-Scale Image Recognition self-linksurface differs ⓘ

this entity surface form: VGG paper

Very Deep Convolutional Networks for Large-Scale Image Recognition → shows → Very Deep Convolutional Networks for Large-Scale Image Recognition self-linksurface differs ⓘ

this entity surface form: VGG-16 and VGG-19 achieve state-of-the-art performance on ImageNet at the time of publication

All labels observed (3)

How this entity was disambiguated Show

Statements (46)

How these facts were elicited Show

Referenced by (6)

How this entity was disambiguated

How these facts were elicited