Statements (59)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:neural_networks
|
| gptkbp:applies_to |
image classification
|
| gptkbp:can_handle |
unseen classes
|
| gptkbp:designed_for |
few-shot learning
|
| gptkbp:has_achieved |
state-of-the-art performance
|
| https://www.w3.org/2000/01/rdf-schema#label |
Matching networks
|
| gptkbp:improves |
sample efficiency
|
| gptkbp:input_output |
similarity scores
|
| gptkbp:introduced |
Vinyals et al.
|
| gptkbp:is_based_on |
metric learning
similarity learning |
| gptkbp:is_compared_to |
gptkb:Siamese_networks
transfer learning traditional supervised learning |
| gptkbp:is_evaluated_by |
gptkb:MS_COCO_dataset
gptkb:Stanford_Dogs_dataset gptkb:Celeb_A_dataset gptkb:CIFAR-100_dataset Oxford Pets dataset CUB-200-2011 dataset Caltech-101 dataset Fashion-MNIST dataset Omniglot dataset mini Image Net dataset VGGFace dataset |
| gptkbp:is_implemented_in |
gptkb:Tensor_Flow
gptkb:Py_Torch |
| gptkbp:is_inspired_by |
human learning
|
| gptkbp:is_related_to |
meta-learning
|
| gptkbp:is_trained_in |
gradient descent
|
| gptkbp:is_used_for |
object detection
image retrieval semantic segmentation |
| gptkbp:is_used_in |
gptkb:virtual_reality
gptkb:vehicles gptkb:robotics gptkb:smart_home_devices healthcare finance augmented reality computer vision natural language processing edge computing speech recognition sentiment analysis recommendation systems time series forecasting anomaly detection social media analysis text classification Io T applications game AI |
| gptkbp:requires |
few training examples
support set |
| gptkbp:uses |
attention mechanism
|
| gptkbp:utilizes |
prototypical networks
|
| gptkbp:year_established |
gptkb:2016
|
| gptkbp:bfsParent |
gptkb:Few-Shot_Learning
|
| gptkbp:bfsLayer |
6
|