mlsanityData to 7 Oct 2026

Meta-Learning in Neural Networks: A Survey

arXiv 2004.05439 ↗First posted 11 Apr 2020cs.LG stat.MLcited by 944

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Abstract (arXiv)

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.

Key facts. Cited by 944 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 155; 2022: 186; 2023: 205; 2024: 157; 2025: 160; 2026: 81.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  2. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  3. 2021Domain Generalization: A Surveycited by 760
  4. 2022What Can Transformers Learn In-Context? A Case Study of Simple Function Classescited by 700
  5. 2021Towards Personalized Federated Learningcited by 462
  6. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  7. 2021Deep Long-Tailed Learning: A Surveycited by 416
  8. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 353
  9. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  10. 2021A Survey on Deep Semi-supervised Learningcited by 250
  11. 2021Online Continual Learning in Image Classification: An Empirical Surveycited by 242
  12. 2021Learning to Optimize: A Primer and A Benchmarkcited by 206
38 more
  1. 2023Knowledge Editing for Large Language Models: A Surveycited by 189
  2. 2024Multi-Step Reasoning with Large Language Models, a Surveycited by 140
  3. 2023Neural Architecture Search: Insights from 1000 Paperscited by 136
  4. 2021Fine-Grained Image Analysis with Deep Learning: A Surveycited by 135
  5. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  6. 2025A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyondcited by 131
  7. 2023End-to-End Speech Recognition: A Surveycited by 127
  8. 2022Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Differencecited by 123
  9. 2025REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewardscited by 121
  10. 2023A Comprehensive Survey of Dataset Distillationcited by 114
  11. 2022Fusing finetuned models for better pretrainingcited by 110
  12. 2021Real-time Neural Radiance Caching for Path Tracingcited by 91
  13. 2021Parameter Prediction for Unseen Deep Architecturescited by 89
  14. 2023CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalizationcited by 88
  15. 2023ATT3D: Amortized Text-to-3D Object Synthesiscited by 87
  16. 2022Head2Toe: Utilizing Intermediate Representations for Better Transfer Learningcited by 83
  17. 2022Tutorial on amortized optimizationcited by 83
  18. 2022Neural-Fly Enables Rapid Learning for Agile Flight in Strong Windscited by 80
  19. 2021Cross-domain Few-shot Learning with Task-specific Adapterscited by 79
  20. 2022Transformers as Meta-Learners for Implicit Neural Representationscited by 79
  21. 2023CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Notcited by 79
  22. 2023In-Context Learning through the Bayesian Prismcited by 78
  23. 2023Machine learning with data assimilation and uncertainty quantification for dynamical systems: a reviewcited by 74
  24. 2022Auto-Lambda: Disentangling Dynamic Task Relationshipscited by 73
  25. 2021Learning Fast Sample Re-weighting Without Reward Datacited by 69
  26. 2023Towards Omni-generalizable Neural Methods for Vehicle Routing Problemscited by 68
  27. 2021MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaptioncited by 65
  28. 2022In Defense of the Unitary Scalarization for Deep Multi-Task Learningcited by 65
  29. 2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
  30. 2021Recurrent Mask Refinement for Few-Shot Medical Image Segmentationcited by 63
  31. 2022Accelerating Dataset Distillation via Model Augmentationcited by 62
  32. 2021Lifelong Person Re-Identification via Adaptive Knowledge Accumulationcited by 57
  33. 2022ColD Fusion: Collaborative Descent for Distributed Multitask Finetuningcited by 52
  34. 2023Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimescited by 52
  35. 2021Meta-learning PINN loss functionscited by 51
  36. 2021Rectifying the Shortcut Learning of Background for Few-Shot Learningcited by 51
  37. 2021StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrievalcited by 50
  38. 2021Reordering Examples Helps during Priming-based Few-Shot Learningcited by 50

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