mlsanityData to 7 Oct 2026

Learning from Few Examples: A Summary of Approaches to Few-Shot Learning

arXiv 2203.04291 ↗First posted 7 Mar 2022cs.LG cs.CVcited by 86

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Abstract (arXiv)

Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Requiring a large number of data samples, many deep learning solutions suffer from data hunger and extensively high computation time and resources. Furthermore, data is often not available due to not only the nature of the problem or privacy concerns but also the cost of data preparation. Data collection, preprocessing, and labeling are strenuous human tasks. Therefore, few-shot learning that could drastically reduce the turnaround time of building machine learning applications emerges as a low-cost solution. This survey paper comprises a representative list of recently proposed few-shot learning algorithms. Given the learning dynamics and characteristics, the approaches to few-shot learning problems are discussed in the perspectives of meta-learning, transfer learning, and hybrid approaches (i.e., different variations of the few-shot learning problem).

Key facts. Cited by 86 papers in the map (data through 7 Oct 2026). Builds on 63 of its 97 references that are in the map. First posted on arXiv 7 Mar 2022.

Builds on

63 of 97 references.

References · 63

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  5. 2013Auto-Encoding Variational Bayescited by 3,980
  6. 2016Wide Residual Networkscited by 3,057
  7. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  8. 2016Matching Networks for One Shot Learningcited by 2,246
  9. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  10. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  11. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  12. 2019A Closer Look at Few-shot Classificationcited by 679
38 more
  1. 2014Neural Turing Machinescited by 630
  2. 2016Learning to learn by gradient descent by gradient descentcited by 616
  3. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  4. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  5. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 435
  6. 2018TADAM: Task dependent adaptive metric for improved few-shot learningcited by 408
  7. 2018Dynamic Few-Shot Visual Learning without Forgettingcited by 385
  8. 2014Memory Networkscited by 354
  9. 2019Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examplescited by 312
  10. 2018How to train your MAMLcited by 306
  11. 2018Meta-Transfer Learning for Few-Shot Learningcited by 306
  12. 2019A Baseline for Few-Shot Image Classificationcited by 298
  13. 2017Meta Networkscited by 268
  14. 2018Few-shot Object Detection via Feature Reweightingcited by 265
  15. 2017Zero-Shot Learning -- The Good, the Bad and the Uglycited by 254
  16. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  17. 2017One-Shot Imitation Learningcited by 232
  18. 2016Key-Value Memory Networks for Directly Reading Documentscited by 223
  19. 2019Cross Attention Network for Few-shot Classificationcited by 218
  20. 2018Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learningcited by 208
  21. 2019Few-Shot Object Detection with Attention-RPN and Multi-Relation Detectorcited by 206
  22. 2019SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learningcited by 194
  23. 2020Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformationcited by 183
  24. 2018Low-Shot Learning from Imaginary Datacited by 181
  25. 2016Learning a Deep Embedding Model for Zero-Shot Learningcited by 153
  26. 2018Task-Agnostic Meta-Learning for Few-shot Learningcited by 151
  27. 2019Edge-labeling Graph Neural Network for Few-shot Learningcited by 114
  28. 2019Meta-GNN: On Few-shot Node Classification in Graph Meta-learningcited by 105
  29. 2019Finding Task-Relevant Features for Few-Shot Learning by Category Traversalcited by 101
  30. 2019Adaptive Cross-Modal Few-Shot Learningcited by 95
  31. 2019TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learningcited by 90
  32. 2020Laplacian Regularized Few-Shot Learningcited by 90
  33. 2018RepMet: Representative-based metric learning for classification and one-shot object detectioncited by 85
  34. 2019Learning to Self-Train for Semi-Supervised Few-Shot Classificationcited by 83
  35. 2018LSTD: A Low-Shot Transfer Detector for Object Detectioncited by 77
  36. 2019Hierarchically Structured Meta-learningcited by 67
  37. 2018Memory Matching Networks for One-Shot Image Recognitioncited by 65
  38. 2018Incremental Few-Shot Learning with Attention Attractor Networkscited by 65

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 18; 2024: 27; 2025: 31; 2026: 5.

86 papers.

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  1. 2024Many-Shot In-Context Learning in Multimodal Foundation Modelscited by 64
  2. 2023Mask-guided BERT for Few Shot Text Classificationcited by 25
  3. 2024Retrieval Augmented Generation for Domain-specific Question Answeringcited by 22
  4. 2024Eureka: Evaluating and Understanding Large Foundation Modelscited by 18
  5. 2022Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overviewcited by 16
  6. 2023Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Surveycited by 14
  7. 2023Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Datacited by 13
  8. 2023Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentationcited by 12
  9. 2023CDFSL-V: Cross-Domain Few-Shot Learning for Videoscited by 12
  10. 2023Towards Task Sampler Learning for Meta-Learningcited by 11
  11. 2024CtRNet-X: Camera-to-Robot Pose Estimation in Real-world Conditions Using a Single Cameracited by 10
  12. 2025DatasetResearch: Benchmarking Agent Systems for Demand-Driven Dataset Discoverycited by 9
38 more
  1. 2022Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparisoncited by 8
  2. 2023Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labelscited by 8
  3. 2024FEET: A Framework for Evaluating Embedding Techniquescited by 8
  4. 2024A Survey of Few-Shot Learning for Biomedical Time Seriescited by 7
  5. 2025FlySearch: Exploring how vision-language models explorecited by 7
  6. 2023You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentationcited by 6
  7. 2024Opportunities and Challenges of Generative-AI in Financecited by 6
  8. 2025Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitationscited by 6
  9. 2025Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impactcited by 6
  10. 2025From Tiny Machine Learning to Tiny Deep Learning: A Surveycited by 5
  11. 2023Domain Adaptive Few-Shot Open-Set Learningcited by 4
  12. 2024Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanismscited by 4
  13. 2024Few-Shot Task Learning through Inverse Generative Modelingcited by 4
  14. 2025GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classificationcited by 4
  15. 2025Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Reviewcited by 4
  16. 2025Schema for In-Context Learningcited by 4
  17. 2024Image-to-Lidar Relational Distillation for Autonomous Driving Datacited by 3
  18. 2024VideoSAM: Open-World Video Segmentationcited by 3
  19. 2025Feedforward Few-shot Species Range Estimationcited by 3
  20. 2025Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imagingcited by 3
  21. 2025ThreatGPT: An Agentic AI Framework for Enhancing Public Safety through Threat Modelingcited by 3
  22. 2023Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compressioncited by 2
  23. 2023Cases of EFL Secondary Students' Prompt Engineering Pathways to Complete a Writing Task with ChatGPTcited by 2
  24. 2023TinyMetaFed: Efficient Federated Meta-Learning for TinyMLcited by 2
  25. 2024Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Promptingcited by 2
  26. 2024Self-supervised Learning for Acoustic Few-Shot Classificationcited by 2
  27. 2024LLM-DER:A Named Entity Recognition Method Based on Large Language Models for Chinese Coal Chemical Domaincited by 2
  28. 2024Balancing Label Quantity and Quality for Scalable Elicitationcited by 2
  29. 2024Active Prompt Tuning Enables Gpt-40 To Do Efficient Classification Of Microscopy Imagescited by 2
  30. 2025Communication styles and reader preferences of LLM- and human-authored COVID-19 information explanations: a case studycited by 2
  31. 2025Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvementscited by 2
  32. 2025Exploring Pre-training Across Domains for Few-Shot Surgical Skill Assessmentcited by 2
  33. 2025Judge Before Answer: Can MLLM Discern the False Premise in Question?cited by 2
  34. 2022Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learningcited by 1
  35. 2023ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysiscited by 1
  36. 2023Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learningcited by 1
  37. 2023Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rayscited by 1
  38. 2023Advancing Perception in Artificial Intelligence through Principles of Cognitive Sciencecited by 1

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