miniImageNet
E899067
miniImageNet is a widely used few-shot learning benchmark dataset derived from ImageNet, consisting of small, labeled images across many classes for evaluating meta-learning and one-shot learning algorithms.
All labels observed (1)
| Label | Occurrences |
|---|---|
| miniImageNet canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11003621 — 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: miniImageNet Context triple: [Matching Networks for One Shot Learning, datasetUsed, miniImageNet]
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A.
CIFAR-10
CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
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B.
CIFAR-100
CIFAR-100 is a widely used image classification dataset consisting of 60,000 32×32 color images across 100 object categories, commonly used to benchmark machine learning models.
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C.
CIFAR
CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
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D.
Matching Networks for One Shot Learning
"Matching Networks for One Shot Learning" is a seminal deep learning paper that introduced a metric-based approach for one-shot image classification using attention and memory-augmented neural networks.
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E.
MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
- 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: miniImageNet Target entity description: miniImageNet is a widely used few-shot learning benchmark dataset derived from ImageNet, consisting of small, labeled images across many classes for evaluating meta-learning and one-shot learning algorithms.
-
A.
CIFAR-10
CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
-
B.
CIFAR-100
CIFAR-100 is a widely used image classification dataset consisting of 60,000 32×32 color images across 100 object categories, commonly used to benchmark machine learning models.
-
C.
CIFAR
CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
-
D.
Matching Networks for One Shot Learning
"Matching Networks for One Shot Learning" is a seminal deep learning paper that introduced a metric-based approach for one-shot image classification using attention and memory-augmented neural networks.
-
E.
MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
few-shot learning benchmark
ⓘ
image dataset ⓘ meta-learning benchmark ⓘ |
| accessMethod | constructed from ImageNet image IDs ⓘ |
| benchmarkRole | standard baseline for few-shot learning ⓘ |
| commonlyUsedWithShotCounts |
1-shot
ⓘ
5-shot ⓘ |
| commonlyUsedWithWayCounts |
20-way
ⓘ
5-way ⓘ |
| comparedWith |
CIFAR-FS
NERFINISHED
ⓘ
FC100 NERFINISHED ⓘ tieredImageNet NERFINISHED ⓘ |
| dataFormat | image files with class labels ⓘ |
| derivedFrom | ImageNet NERFINISHED ⓘ |
| evaluationProtocol | episodic training and testing ⓘ |
| hasBenchmarkProperty |
fixed train/validation/test class splits
ⓘ
supports reproducible few-shot experiments ⓘ |
| hasClassBalance | approximately balanced across classes ⓘ |
| hasClassCount | 100 ⓘ |
| hasDataType | natural images ⓘ |
| hasDomain | general object recognition ⓘ |
| hasFewShotSetting | N-way K-shot classification ⓘ |
| hasGranularity | object category level ⓘ |
| hasInputShape | 3x84x84 (channels-first) in many implementations ⓘ |
| hasLabelType | image class labels ⓘ |
| hasLicenseSource | ImageNet license ⓘ |
| hasModality | RGB images ⓘ |
| hasNumberOfClasses | 100 ⓘ |
| hasTask | image classification ⓘ |
| hasTestSplitClassCount | 20 ⓘ |
| hasTotalImageCountApprox | 60000 ⓘ |
| hasTrainSplitClassCount | 64 ⓘ |
| hasTypicalImageResolution | 84x84 pixels ⓘ |
| hasTypicalTrainImagesPerClass | 600 ⓘ |
| hasValidationSplitClassCount | 16 ⓘ |
| introducedFor | benchmarking meta-learning algorithms ⓘ |
| isSubsetOf | ImageNet ILSVRC-2012 dataset NERFINISHED ⓘ |
| isWidelyUsedSince | around 2016 ⓘ |
| popularizedBy |
Matching Networks for One Shot Learning
NERFINISHED
ⓘ
Model-Agnostic Meta-Learning (MAML) NERFINISHED ⓘ Prototypical Networks for Few-shot Learning NERFINISHED ⓘ |
| usedFor |
few-shot learning evaluation
ⓘ
meta-learning evaluation ⓘ one-shot learning evaluation ⓘ |
| usedIn |
computer vision research
ⓘ
machine learning research ⓘ |
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: miniImageNet Description of subject: miniImageNet is a widely used few-shot learning benchmark dataset derived from ImageNet, consisting of small, labeled images across many classes for evaluating meta-learning and one-shot learning algorithms.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.