Group Normalization
E701502
Group Normalization is a neural network normalization technique that divides channels into groups and normalizes within each group to stabilize training, especially effective for small batch sizes.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Group Normalization canonical | 2 |
| Group Normalization (2018) paper | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7874871 — 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: Group Normalization Context triple: [Layer Normalization, relatedTo, Group Normalization]
-
A.
Layer Normalization
Layer Normalization is a neural network normalization technique that stabilizes and accelerates training by normalizing activations across features within each data sample, particularly useful in recurrent and transformer-based models.
-
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.
ShuffleNetV2
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
-
D.
Neural Filters
Neural Filters are Adobe Photoshop’s AI-powered tools that apply advanced, machine-learning-based adjustments and creative effects to images with minimal manual editing.
-
E.
DenseNet
DenseNet is a family of convolutional neural network architectures characterized by densely connected layers that improve information flow and parameter efficiency for image recognition tasks.
- 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: Group Normalization Target entity description: Group Normalization is a neural network normalization technique that divides channels into groups and normalizes within each group to stabilize training, especially effective for small batch sizes.
-
A.
Layer Normalization
Layer Normalization is a neural network normalization technique that stabilizes and accelerates training by normalizing activations across features within each data sample, particularly useful in recurrent and transformer-based models.
-
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.
ShuffleNetV2
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
-
D.
Neural Filters
Neural Filters are Adobe Photoshop’s AI-powered tools that apply advanced, machine-learning-based adjustments and creative effects to images with minimal manual editing.
-
E.
DenseNet
DenseNet is a family of convolutional neural network architectures characterized by densely connected layers that improve information flow and parameter efficiency for image recognition tasks.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
deep learning method
ⓘ
neural network normalization technique ⓘ |
| advantage |
does not require running estimates of statistics
ⓘ
independent of batch dimension ⓘ more stable training in memory-constrained settings ⓘ performance is less sensitive to batch size ⓘ works well with very small batch sizes ⓘ |
| advantageOver | Batch Normalization ⓘ |
| appliesTo |
convolutional neural networks
ⓘ
feedforward neural networks ⓘ sequence models ⓘ vision models ⓘ |
| citationYear | 2018 ⓘ |
| comparedTo |
Batch Normalization
ⓘ
Instance Normalization ⓘ Layer Normalization ⓘ |
| describedIn |
Group Normalization (2018) paper
ⓘ
linked to:
Group Normalization
|
| doesNotDependOn | batch dimension statistics ⓘ |
| field |
computer vision
ⓘ
deep learning ⓘ |
| goal |
improve optimization of deep networks
ⓘ
reduce internal covariate shift ⓘ |
| hyperparameter | group size ⓘ |
| implementationDetail |
groups are formed by splitting channels along the channel dimension
ⓘ
mean and variance are computed over spatial dimensions and group channels ⓘ |
| includesParameter |
learnable scale (gamma)
ⓘ
learnable shift (beta) ⓘ |
| introducedBy |
Kaiming He
ⓘ
Yuxin Wu ⓘ |
| keyIdea | divides channels into groups and normalizes within each group ⓘ |
| motivation |
reduce dependence on batch size
ⓘ
stabilize training for small batch sizes ⓘ |
| normalizationAxis | channel groups ⓘ |
| normalizes | activations within each group ⓘ |
| oftenUsedWith |
ResNet architectures
ⓘ
linked to:
ResNet
convolutional layers ⓘ object detection models ⓘ segmentation models ⓘ |
| operatesOn | feature channels ⓘ |
| publishedAt |
ECCV 2018
ⓘ
linked to:
European Conference on Computer Vision
|
| relatedConcept |
Batch Normalization
ⓘ
Instance Normalization ⓘ Layer Normalization ⓘ |
| typicalSetting |
detection and segmentation tasks with large images
ⓘ
small-batch training on GPUs ⓘ |
| usesParameter | number of groups ⓘ |
| usesStatistics |
per-group mean
ⓘ
per-group variance ⓘ |
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: Group Normalization Description of subject: Group Normalization is a neural network normalization technique that divides channels into groups and normalizes within each group to stabilize training, especially effective for small batch sizes.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Group Normalization