ResNet

E74928

convolutional neural network architecture deep learning model residual network

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.

Try in SPARQL Jump to: Surface forms Statements Referenced by

All labels observed (14)

Label	Occurrences
ResNet canonical	11
Deep Residual Learning for Image Recognition	6
Deep Residual Learning for Image Recognition (co-author)	1
Pre-activation ResNet	1
ResNet architecture	1
ResNet on ImageNet classification	1
ResNet-101	1
ResNet-152	1
ResNet-18	1
ResNet-34	1
ResNet-50	1
ResNet-v2	1
Wide ResNet	1
residual networks	1

Statements (55)

Predicate	Object
instanceOf	convolutional neural network architecture ⓘ deep learning model ⓘ residual network ⓘ
achievedResult	state-of-the-art performance on ImageNet at introduction ⓘ
addresses	degradation problem in deep networks ⓘ vanishing gradient problem ⓘ
affiliationOfAuthors	Microsoft Research Cambridge ⓘ surface form: Microsoft Research
benchmarkedOn	CIFAR-10 ⓘ CIFAR-100 ⓘ ImageNet ⓘ
developedBy	Jian Sun ⓘ Kaiming He ⓘ Shaoqing Ren ⓘ Xiangyu Zhang ⓘ
domain	computer vision ⓘ
enables	training of very deep networks ⓘ
field	deep learning ⓘ
firstPublishedVenue	IEEE Computer Society Conference on Computer Vision and Pattern Recognition ⓘ surface form: CVPR 2016
firstPublishedYear	2015 ⓘ
hasArchitectureDepth	101 layers ⓘ 152 layers ⓘ 18 layers ⓘ 34 layers ⓘ 50 layers ⓘ
hasConnectionType	identity skip connection ⓘ projection shortcut ⓘ
hasKeyIdea	identity mappings ⓘ residual learning ⓘ skip connections ⓘ
hasVariant	ResNet self-linksurface differs ⓘ surface form: Pre-activation ResNet ResNeXt ⓘ ResNet self-linksurface differs ⓘ surface form: ResNet-101 ResNet self-linksurface differs ⓘ surface form: ResNet-152 ResNet self-linksurface differs ⓘ surface form: ResNet-18 ResNet self-linksurface differs ⓘ surface form: ResNet-34 ResNet self-linksurface differs ⓘ surface form: ResNet-50 ResNet self-linksurface differs ⓘ surface form: ResNet-v2 ResNet self-linksurface differs ⓘ surface form: Wide ResNet
implementationAvailableIn	Keras ⓘ MXNet ⓘ PyTorch ⓘ TensorFlow ⓘ
influenced	design of modern computer vision backbones ⓘ
inspired	many subsequent CNN architectures ⓘ
introducedInPaper	ResNet self-linksurface differs ⓘ surface form: Deep Residual Learning for Image Recognition
optimizationBenefit	eases optimization of deep networks ⓘ
trainingMethod	stochastic gradient descent ⓘ
usedAs	backbone network in detection models ⓘ
usedFor	feature extraction ⓘ image classification ⓘ object detection ⓘ
usesComponent	ReLU activation ⓘ batch normalization ⓘ convolutional layer ⓘ residual block ⓘ

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: ResNet
Description of subject: 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.

Referenced by (29)

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

LeNet → influenced → ResNet ⓘ

AlexNet → influenced → ResNet ⓘ

VGG → influenced → ResNet ⓘ

ResNet → introducedInPaper → ResNet self-linksurface differs ⓘ

this entity surface form: Deep Residual Learning for Image Recognition