Inception architecture

E107999

convolutional neural network architecture deep learning model 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.

Try in SPARQL Jump to: Surface forms Statements Referenced by

All labels observed (11)

Label	Occurrences
Going Deeper with Convolutions	2
GoogLeNet	2
Inception architecture canonical	2
Inception network	2
Rethinking the Inception Architecture for Computer Vision	2
Inception modules	1
Inception v3	1
Inception-ResNet	1
Inception-based architectures	1
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning	1
InceptionV3	1

Statements (48)

Predicate	Object
instanceOf	convolutional neural network architecture ⓘ deep learning model architecture ⓘ
achievedResult	won ILSVRC 2014 image classification challenge via GoogLeNet ⓘ
commonlyUsedWith	ReLU activation functions ⓘ batch normalization ⓘ softmax output layer ⓘ
designPrinciple	balancing depth and width of networks ⓘ computational cost optimization under fixed resource budget ⓘ multi-branch convolutional paths with different receptive field sizes ⓘ
developedAt	Google ⓘ
field	computer vision ⓘ deep learning ⓘ machine learning ⓘ
goal	achieve state-of-the-art image recognition performance ⓘ improve computational efficiency of deep CNNs ⓘ
hasKeyFeature	1x1 convolutions for dimensionality reduction ⓘ Inception architecture self-linksurface differs ⓘ surface form: Inception modules factorized convolutions ⓘ parallel multi-scale processing ⓘ sparse connections approximated by dense operations ⓘ
hasVariant	Inception v1 ⓘ Inception v2 ⓘ Inception architecture self-linksurface differs ⓘ surface form: Inception v3 Inception v4 ⓘ Inception architecture self-linksurface differs ⓘ surface form: Inception-ResNet
influenced	later efficient CNN architectures ⓘ multi-branch network designs ⓘ
inspiredBy	Network-in-Network architecture ⓘ
introducedBy	Andrew Rabinovich ⓘ Christian Szegedy ⓘ Dragomir Anguelov ⓘ Dumitru Erhan ⓘ Pierre Sermanet ⓘ Scott Reed ⓘ Vincent Vanhoucke ⓘ Wei Liu ⓘ Yangqing Jia ⓘ
introducedIn	GoogLeNet ⓘ
introducedInPaper	Inception architecture self-linksurface differs ⓘ surface form: Going Deeper with Convolutions
notableProperty	good accuracy–computation trade-off ⓘ scales well to large datasets like ImageNet ⓘ
optimizationMethod	stochastic gradient descent ⓘ
paperPublishedAt	IEEE Computer Society Conference on Computer Vision and Pattern Recognition ⓘ surface form: CVPR 2015
typicalInputDomain	natural images ⓘ
usedFor	feature extraction ⓘ image classification ⓘ image recognition ⓘ object detection ⓘ

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: Inception architecture
Description of subject: 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.

Referenced by (16)

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

Christian Szegedy → notableWork → Inception architecture ⓘ

this entity surface form: Going Deeper with Convolutions

Christian Szegedy → notableWork → Inception architecture ⓘ

this entity surface form: Rethinking the Inception Architecture for Computer Vision

Christian Szegedy → notableWork → Inception architecture ⓘ

this entity surface form: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

AlexNet → influenced → Inception architecture ⓘ

this entity surface form: GoogLeNet

VGG → influenced → Inception architecture ⓘ

this entity surface form: Inception-based architectures

torchvision (ecosystem) → modelFamily → Inception architecture ⓘ

subject surface form: torchvision

this entity surface form: GoogLeNet

torchvision (ecosystem) → modelFamily → Inception architecture ⓘ

subject surface form: torchvision

this entity surface form: InceptionV3

Inception architecture → hasKeyFeature → Inception architecture self-linksurface differs ⓘ

this entity surface form: Inception modules

Inception architecture → introducedInPaper → Inception architecture self-linksurface differs ⓘ

this entity surface form: Going Deeper with Convolutions

Inception architecture → hasVariant → Inception architecture self-linksurface differs ⓘ

this entity surface form: Inception v3

Inception architecture → hasVariant → Inception architecture self-linksurface differs ⓘ

this entity surface form: Inception-ResNet

Zbigniew Wojna → notableWork → Inception architecture ⓘ

this entity surface form: Rethinking the Inception Architecture for Computer Vision

Inception Score → basedOn → Inception architecture ⓘ

this entity surface form: Inception network

Fréchet Inception Distance → uses → Inception architecture ⓘ

this entity surface form: Inception network

ResNeXt → relatedTo → Inception architecture ⓘ