Very Deep Convolutional Networks for Large-Scale Image Recognition
E366102
"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.
All labels observed (5)
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 |
ILSVRC-2014
ⓘ
linked to:
ImageNet
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-11 architecture
ⓘ
linked to:
VGG
VGG-13 architecture ⓘ
linked to:
VGG
VGG-16 architecture ⓘ
linked to:
VGG
VGG-19 architecture ⓘ
linked to:
VGG
|
| shortTitle | VGG paper ⓘ |
| shows |
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 ⓘ |
| uses |
1x1 convolutional filters
ⓘ
3x3 convolutional filters ⓘ max pooling layers ⓘ |
| year | 2014 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Very Deep Convolutional Networks for Large-Scale Image Recognition Description of subject: "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.
Referenced by (8)
Full triples — surface form annotated when it differs from this entity's canonical label.
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
ⓘ
linked to: Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
→
shows
→
VGG-16 and VGG-19 achieve state-of-the-art performance on ImageNet at the time of publication
ⓘ
linked to: Very Deep Convolutional Networks for Large-Scale Image Recognition
Karén Simonyan
→
knownFor
→
VGGNet paper "Very Deep Convolutional Networks for Large-Scale Image Recognition"
ⓘ
linked to: Very Deep Convolutional Networks for Large-Scale Image Recognition
Karén Simonyan
→
notableWork
→
"Very Deep Convolutional Networks for Large-Scale Image Recognition"
ⓘ
linked to: Very Deep Convolutional Networks for Large-Scale Image Recognition