Matching networks

GPTKB entity

Statements (59)
Predicate Object
gptkbp:instance_of gptkb:microprocessor
gptkbp:bfsLayer 5
gptkbp:bfsParent gptkb:Few-Shot_Learning
gptkbp:applies_to image classification
gptkbp:based_on metric learning
similarity learning
gptkbp:controls unseen classes
gptkbp:has_achievements 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:inspired_by human learning
gptkbp:introduced Vinyals et al.
gptkbp:is_compared_to gptkb:Siamese_networks
transfer learning
traditional supervised learning
gptkbp:is_designed_for few-shot learning
gptkbp:is_evaluated_by gptkb:Stanford_Dogs_dataset
gptkb:Celeb_A_dataset
gptkb:CIFAR-100_dataset
MSCOCO dataset
Oxford Pets dataset
CUB-200-2011 dataset
Caltech-101 dataset
Fashion-MNIST dataset
Omniglot dataset
VGG Face dataset
mini Image Net dataset
gptkbp:is_implemented_in gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
gptkbp:is_related_to meta-learning
gptkbp:is_used_for object detection
image retrieval
semantic segmentation
gptkbp:is_used_in gptkb:musician
gptkb:robot
gptkb:computer
gptkb:engine
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:training gradient descent
gptkbp:uses attention mechanism
gptkbp:utilizes prototypical networks
gptkbp:year_created gptkb:2016