Prototypical Networks for Few-shot Learning

Jake Snell; Kevin Swersky; Richard S. Zemel

doi:https://doi.org/10.48550/arxiv.1703.05175

Prototypical Networks for Few-shot Learning

2017 · arXiv (Cornell University) · 5,204 citations · 9 from inside this corpus

Jake Snell low, Kevin Swersky low, Richard S. Zemel

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

9 of 9 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

this paper works it cites works citing it node size = global citations · hover for the full title

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

Advances and Open Problems in Federated Learning→Prototypical Networks for Few-shot LearningLegacy edge — source detail unavailable
[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]→Prototypical Networks for Few-shot LearningLegacy edge — source detail unavailable
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Langu…→Prototypical Networks for Few-shot LearningLegacy edge — source detail unavailable
Unsupervised Feature Learning via Non-parametric Instance Discrimination→Prototypical Networks for Few-shot LearningLegacy edge — source detail unavailable
Generalizing from a Few Examples→Prototypical Networks for Few-shot LearningLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→Prototypical Networks for Few-shot LearningLegacy edge — source detail unavailable
Relational Knowledge Distillation→Prototypical Networks for Few-shot LearningLegacy edge — source detail unavailable
Automated Machine Learning→Prototypical Networks for Few-shot LearningLegacy edge — source detail unavailable
PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment→Prototypical Networks for Few-shot LearningLegacy edge — source detail unavailable
PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment→[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]Legacy edge — source detail unavailable
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Langu…→Generalizing from a Few ExamplesLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→Unsupervised Feature Learning via Non-parametric Instance DiscriminationLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]Legacy edge — source detail unavailable

What cites it, inside the corpus

Paper	Year	Cited
Advances and Open Problems in Federated Learning	2020	5,383
[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]		4,858
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Langu…	2022	3,836
Unsupervised Feature Learning via Non-parametric Instance Discrimination	2018	3,640
Generalizing from a Few Examples	2020	2,761
On the Opportunities and Risks of Foundation Models	2021	2,265
Relational Knowledge Distillation	2019	1,669
Automated Machine Learning	2019	1,451
PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment	2019	1,431

Links

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Topics

Domain Adaptation and Few-Shot Learning

Computer Science

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partial

One field of this record is missing or disagrees with another. What is shown below is what the source publishes.

supports3 author record(s) attached.
weakensNo references are recorded despite 5,204 citations. A paper this heavily cited did not cite nothing, so the record is incomplete.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

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