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Cited by 886Domain Adaptation and Few-Shot LearningMachine Learning and AlgorithmsReinforcement Learning in Robotics

Meta-Learning in Neural Networks: A Survey

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Hospedales, Timothy M, Antoniou, Antreas, Micaelli, Paul, Storkey, Amos J.·2021·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2021.3079209
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Abstract

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization. This survey describes the contemporary meta-learning landscape. We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning and hyperparameter optimization. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today. We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning. Finally, we discuss outstanding challenges and promising areas for future research.

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  • Meta-learning leverages experiences distilled from multiple learning episodes, encompassing a distribution of related tasks, to enhance future learning performance.

    “Meta-learning leverages experiences distilled from multiple learning episodes, encompassing a distribution of related tasks, to enhance future learning performance [94].”

    From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [94]

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Authors
Hospedales, T. M., Antoniou, A., Micaelli, P., & Storkey, A. J.
Affiliations
University of Edinburgh (GB)
ORCID
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021
Volume
44
Issue
9
Pages
1-1
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
  • 1939-3539
DOI
10.1109/tpami.2021.3079209
OpenAlex
W3015606043
Cited by
886
References
26
Citations by year
2026: 24 · 2025: 155 · 2024: 189 · 2023: 192 · 2022: 109 · 2021: 169 · 2020: 45 · 2019: 3
FWCI
58.22
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Engineering and Physical Sciences Research Council (EP/S000631/1, EP/R026173/1, EP/R026173)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords
meta-learning, artificial neural networks, transfer learning, hyperparameter optimization, few-shot learning, reinforcement learning, generalization
MeSH terms
Algorithms, Neural Networks, Computer

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