mlsanityData to 7 Oct 2026

A Survey of Few-Shot Learning for Biomedical Time Series

arXiv 2405.02485 ↗First posted 3 May 2024cs.LG cs.AIcited by 7

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Abstract (arXiv)

Advancements in wearable sensor technologies and the digitization of medical records have contributed to the unprecedented ubiquity of biomedical time series data. Data-driven models have tremendous potential to assist clinical diagnosis and improve patient care by improving long-term monitoring capabilities, facilitating early disease detection and intervention, as well as promoting personalized healthcare delivery. However, accessing extensively labeled datasets to train data-hungry deep learning models encounters many barriers, such as long-tail distribution of rare diseases, cost of annotation, privacy and security concerns, data-sharing regulations, and ethical considerations. An emerging approach to overcome the scarcity of labeled data is to augment AI methods with human-like capabilities to leverage past experiences to learn new tasks with limited examples, called few-shot learning. This survey provides a comprehensive review and comparison of few-shot learning methods for biomedical time series applications. The clinical benefits and limitations of such methods are discussed in relation to traditional data-driven approaches. This paper aims to provide insights into the current landscape of few-shot learning for biomedical time series and its implications for future research and applications.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 65 of its 165 references that are in the map. First posted on arXiv 3 May 2024.

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65 of 165 references.

References · 65

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  6. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  7. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  8. 2016Densely Connected Convolutional Networkscited by 6,691
  9. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  10. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  11. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  12. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
38 more
  1. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  5. 2016Matching Networks for One Shot Learningcited by 2,246
  6. 2014Conditional Generative Adversarial Netscited by 2,152
  7. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  8. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 832
  9. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  10. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 616
  11. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  12. 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 566
  13. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  14. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 460
  15. 2018The UCR Time Series Archivecited by 421
  16. 2019Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examplescited by 312
  17. 2022A Transformer-Based Siamese Network for Change Detectioncited by 243
  18. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  19. 2023Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learnerscited by 152
  20. 2019Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processescited by 131
  21. 2020Few-Shot Classification with Feature Map Reconstruction Networkscited by 116
  22. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
  23. 2020Improved Techniques for Training Single-Image GANscited by 77
  24. 2016Deep Learning Human Mind for Automated Visual Classificationcited by 76
  25. 2023Large Language Models Enable Few-Shot Clusteringcited by 72
  26. 2021Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learningcited by 70
  27. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  28. 2021Few-shot Network Anomaly Detection via Cross-network Meta-learningcited by 64
  29. 2020Few-shot Scene-adaptive Anomaly Detectioncited by 62
  30. 2016UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphonescited by 54
  31. 2023A Survey on Few-Shot Class-Incremental Learningcited by 48
  32. 2022GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learningcited by 35
  33. 2019MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Recordscited by 33
  34. 2023A Systematic Review of Few-Shot Learning in Medical Imagingcited by 28
  35. 2022HyperShot: Few-Shot Learning by Kernel HyperNetworkscited by 24
  36. 2019Meta-Learning for Few-Shot Time Series Classificationcited by 23
  37. 2020Sense and Learn: Self-Supervision for Omnipresent Sensorscited by 21
  38. 2022MetaPrompting: Learning to Learn Better Promptscited by 19

50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 1.

7 papers.

  1. 2025From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detectioncited by 3
  2. 2024Feasibility of Detecting Cognitive Impairment and Psychological Well-being among Older Adults Using Facial, Acoustic, Linguistic, and Cardiovascular Patterns Derived from Remote Conversationscited by 2
  3. 2025Federated Few-Shot Learning for Epileptic Seizure Detection Under Privacy Constraintscited by 1
  4. 2025Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecastingcited by 0
  5. 2025DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignalscited by 0
  6. 2025QuanvNeXt: An end-to-end quanvolutional neural network for EEG-based detection of major depressive disordercited by 0
  7. 2026FETERS: Few-Shot Early Time-Series Classification via Effective Ratio Selectioncited by 0