Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Chelsea Finn; Pieter Abbeel; Sergey Levine

doi:https://doi.org/10.48550/arxiv.1703.03400

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

2017 · arXiv (Cornell University) · 5,794 citations · 7 from inside this corpus

Chelsea Finn, Pieter Abbeel, Sergey Levine

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.

14 of 14 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→Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksLegacy edge — source detail unavailable
[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]→Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksLegacy edge — source detail unavailable
Relational inductive biases, deep learning, and graph networks→Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksLegacy edge — source detail unavailable
Shortcut learning in deep neural networks→Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksLegacy edge — source detail unavailable
Automated Machine Learning→Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksLegacy edge — source detail unavailable
PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment→Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksLegacy edge — source detail unavailable
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks→[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…Legacy edge — source detail unavailable
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks→Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…Legacy edge — source detail unavailable
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks→Simple statistical gradient-following algorithms for connectionist reinforcement learningLegacy edge — source detail unavailable
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks→DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionLegacy edge — source detail unavailable
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks→Siamese Neural Networks for One-shot Image RecognitionLegacy edge — source detail unavailable
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks→Trust Region Policy OptimizationLegacy edge — source detail unavailable
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks→Optimization as a Model for Few-Shot LearningLegacy edge — source detail unavailable
Optimization as a Model for Few-Shot Learning→DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionLegacy edge — source detail unavailable
Optimization as a Model for Few-Shot Learning→[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…Legacy edge — source detail unavailable
Optimization as a Model for Few-Shot Learning→Siamese Neural Networks for One-shot Image RecognitionLegacy edge — source detail unavailable
[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]→Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…Legacy edge — source detail unavailable
[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]→Optimization as a Model for Few-Shot LearningLegacy edge — source detail unavailable
[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]→[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…Legacy edge — source detail unavailable
[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]→Siamese Neural Networks for One-shot Image RecognitionLegacy edge — source detail unavailable
PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment→Optimization as a Model 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
Advances and Open Problems in Federated Learning→Optimization as a Model for Few-Shot LearningLegacy 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
On the Opportunities and Risks of Foundation Models→Shortcut learning in deep neural networksLegacy edge — source detail unavailable
Automated Machine Learning→Optimization as a Model for Few-Shot LearningLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…	2015	50,396
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…	2015	24,404
Simple statistical gradient-following algorithms for connectionist reinforcement learning	1992	7,519
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition	2013	3,564
Siamese Neural Networks for One-shot Image Recognition	2015	3,253
Trust Region Policy Optimization	2015	3,129
Optimization as a Model for Few-Shot Learning	2017	2,436

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
Relational inductive biases, deep learning, and graph networks	2018	2,406
On the Opportunities and Risks of Foundation Models	2021	2,265
Shortcut learning in deep neural networks	2020	2,040
Automated Machine Learning	2019	1,451
PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment	2019	1,431

Links

Download BibTeX · Download RIS · DOI · Open access copy · OpenAlex record

Topics

Domain Adaptation and Few-Shot Learning	Computer Science
Advanced Neural Network Applications	Computer Science
Multimodal Machine Learning Applications	Computer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

supports3 author record(s) attached.
supports32 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:43+00:00.

sha256 5cad55ac5d4d41e1…