Prototypical Networks

E899064

few-shot learning method metric-based meta-learning method neural network based model supervised learning algorithm

Prototypical Networks are a few-shot learning method that represents each class by the mean of its embedded support examples and classifies queries based on distances to these learned prototypes in embedding space.

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Statements (47)

Predicate	Object
instanceOf	few-shot learning method ⓘ metric-based meta-learning method ⓘ neural network based model ⓘ supervised learning algorithm ⓘ
advantage	non-parametric classification layer ⓘ simple and efficient inference ⓘ
appliedTo	Omniglot dataset ⓘ image classification ⓘ miniImageNet dataset ⓘ
assumes	embedding space where classes form tight clusters ⓘ
basedOn	metric learning ⓘ nearest prototype classification ⓘ
canUse	cosine distance ⓘ squared Euclidean distance ⓘ
citationCountCategory	highly cited in few-shot learning literature ⓘ
classifiesBy	distance to class prototypes ⓘ
codeAvailableAs	open-source implementations in PyTorch ⓘ open-source implementations in TensorFlow ⓘ
comparedWith	MAML NERFINISHED ⓘ Matching Networks NERFINISHED ⓘ
computes	prototype for each class in embedding space ⓘ
describedInPaper	Prototypical Networks for Few-shot Learning NERFINISHED ⓘ
evaluationProtocol	episodic evaluation matching training setup ⓘ
extendedTo	cross-domain few-shot learning ⓘ semi-supervised few-shot learning variants ⓘ zero-shot learning variants ⓘ
inferenceStep	apply softmax over negative distances ⓘ compute distances from query embeddings to prototypes ⓘ compute prototype for each class from support set ⓘ
inspired	many subsequent prototype-based few-shot methods ⓘ
learningObjective	learn embedding where examples cluster around class prototypes ⓘ
optimizedWith	cross-entropy loss over episodic tasks ⓘ
proposedBy	Jake Snell NERFINISHED ⓘ Kevin Swersky NERFINISHED ⓘ Richard Zemel NERFINISHED ⓘ
publicationYear	2017 ⓘ
publishedIn	Neural Information Processing Systems (NeurIPS) NERFINISHED ⓘ
representsClassAs	mean of embedded support examples ⓘ
supports	N-way K-shot classification ⓘ
trainingRegime	episodic few-shot tasks ⓘ
typicallyUses	Euclidean distance ⓘ
uses	class prototypes ⓘ distance metric ⓘ embedding function ⓘ episodic training ⓘ query set ⓘ support set ⓘ

Referenced by (1)

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Matching Networks for One Shot Learning → influenced → Prototypical Networks ⓘ