Data to 7 Oct 2026

A Survey of Deep Meta-Learning

arXiv 2010.03522 ↗First posted 7 Oct 2020cs.LG cs.AI stat.MLcited by 99

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Abstract (arXiv)

Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The field of Deep Meta-Learning advances at great speed, but lacks a unified, in-depth overview of current techniques. With this work, we aim to bridge this gap. After providing the reader with a theoretical foundation, we investigate and summarize key methods, which are categorized into i)~metric-, ii)~model-, and iii)~optimization-based techniques. In addition, we identify the main open challenges, such as performance evaluations on heterogeneous benchmarks, and reduction of the computational costs of meta-learning.

Key facts. Cited by 99 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 16; 2023: 28; 2024: 19; 2025: 17; 2026: 5.

99 papers.

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2021A Survey on Deep Semi-supervised Learningcited by 250
2023Knowledge Editing for Large Language Models: A Surveycited by 189
2024Multi-Step Reasoning with Large Language Models, a Surveycited by 140
2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
2021Multi-Objective Meta Learningcited by 39
2022A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledgecited by 31
2023Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classificationcited by 29
2023Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forwardcited by 28
2024Domain Generalization through Meta-Learning: A Surveycited by 28
2022Meta Learning for Natural Language Processing: A Surveycited by 26
2023Meta-learning approaches for few-shot learning: A survey of recent advancescited by 25
2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 23

38 more

2021An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Datasetcited by 18
2024Graph Neural Networks for Job Shop Scheduling Problems: A Surveycited by 15
2021High-Accuracy Model-Based Reinforcement Learning, a Surveycited by 13
2023Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issuescited by 12
2021Multimodality in Meta-Learning: A Comprehensive Surveycited by 11
2024Training Machine Learning models at the Edge: A Surveycited by 11
2022Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approachcited by 10
2022Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classificationcited by 10
2023Knowledge-enhanced Agents for Interactive Text Gamescited by 10
2022Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methodscited by 9
2023Meta-learning in healthcare: A surveycited by 9
2022Multi-Task Meta Learning: learn how to adapt to unseen taskscited by 8
2022SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognitioncited by 8
2024Meta-Learning Empowered Meta-Face: Personalized Speaking Style Adaptation for Audio-Driven 3D Talking Face Animationcited by 8
2025Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformercited by 8
2021Stateless Neural Meta-Learning using Second-Order Gradientscited by 7
2023Exploring the Space of Key-Value-Query Models with Intentioncited by 7
2025Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignmentcited by 7
2021Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta reviewcited by 6
2022Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learningcited by 6
2023Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learningcited by 6
2023Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalizationcited by 6
2024Machine learning for industrial sensing and control: A survey and practical perspectivecited by 6
2024Survey of Computerized Adaptive Testing: A Machine Learning Perspectivecited by 6
2021Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learningcited by 5
2022Multi-layer Representation Learning for Robust OOD Image Classificationcited by 5
2023MetaViewer: Towards A Unified Multi-View Representationcited by 5
2022Advances in MetaDL: AAAI 2021 challenge and workshopcited by 4
2023A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniquescited by 4
2023ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localizationcited by 4
2023Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learningcited by 4
2023CNN Feature Map Augmentation for Single-Source Domain Generalizationcited by 4
2025Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Reviewcited by 4
2025Overtuning in Hyperparameter Optimizationcited by 4
2025InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarioscited by 4
2021Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximationcited by 3
2023Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNscited by 3
2023Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systemscited by 3

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