NASNet
E899014
NASNet variant
convolutional neural network architecture family
deep learning model
image classification model
neural architecture search result
NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| NASNet canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11003093 — 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: NASNet Context triple: [Neural Architecture Search, notableMethod, NASNet]
-
A.
GoogLeNet
GoogLeNet is a deep convolutional neural network developed by Google that popularized the Inception architecture and achieved state-of-the-art performance in image recognition tasks.
-
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.
SqueezeNet
SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
-
D.
MobileNetV2
MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
-
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: NASNet Target entity description: NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
-
A.
GoogLeNet
GoogLeNet is a deep convolutional neural network developed by Google that popularized the Inception architecture and achieved state-of-the-art performance in image recognition tasks.
-
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.
SqueezeNet
SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
-
D.
MobileNetV2
MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
-
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 (51)
| Predicate | Object |
|---|---|
| instanceOf |
NASNet variant
ⓘ
NASNet variant ⓘ NASNet variant ⓘ NASNet variant ⓘ NASNet variant ⓘ convolutional neural network architecture family ⓘ deep learning model ⓘ image classification model ⓘ neural architecture search result ⓘ |
| achievedStateOfTheArtOn |
CIFAR-10 test set
ⓘ
ImageNet validation set NERFINISHED ⓘ |
| basedOn | neural architecture search ⓘ |
| controllerType | recurrent neural network controller ⓘ |
| developedBy |
Barret Zoph
NERFINISHED
ⓘ
Google Brain NERFINISHED ⓘ Jonathon Shlens NERFINISHED ⓘ Quoc V. Le NERFINISHED ⓘ Vijay Vasudevan NERFINISHED ⓘ |
| field |
automated machine learning
ⓘ
computer vision ⓘ |
| hasDesignPrinciple |
cell-based architecture search
ⓘ
search on small dataset then transfer to large dataset ⓘ |
| hasLicense | Apache License 2.0 (for TensorFlow implementation) NERFINISHED ⓘ |
| hasVariant |
NASNet-A
NERFINISHED
ⓘ
NASNet-A-Mobile NERFINISHED ⓘ NASNet-B NERFINISHED ⓘ NASNet-C NERFINISHED ⓘ NASNet-Large NERFINISHED ⓘ |
| implementedIn | TensorFlow NERFINISHED ⓘ |
| influenced |
AmoebaNet
NERFINISHED
ⓘ
EfficientNet NERFINISHED ⓘ |
| introducedInPaper | Learning Transferable Architectures for Scalable Image Recognition NERFINISHED ⓘ |
| introducedYear | 2017 ⓘ |
| optimizedFor |
accuracy
ⓘ
computational efficiency ⓘ computational efficiency ⓘ high accuracy ⓘ mobile and embedded devices ⓘ |
| paperArchiveId | arXiv:1707.07012 ⓘ |
| searchedOnDataset | CIFAR-10 NERFINISHED ⓘ |
| searchMethod | reinforcement learning controller ⓘ |
| searchSpace | convolutional cell structures ⓘ |
| task | image classification ⓘ |
| top1AccuracyOnImageNetApprox | 82.7% ⓘ |
| top5AccuracyOnImageNetApprox | 96.2% ⓘ |
| transferredToDataset | ImageNet NERFINISHED ⓘ |
| uses |
convolutional layers
ⓘ
normal cell ⓘ reduction cell ⓘ |
| usesRegularization |
batch normalization
ⓘ
dropout ⓘ |
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: NASNet Description of subject: NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.