mlsanityData to 7 Oct 2026

Active One-shot Learning

arXiv 1702.06559 ↗First posted 21 Feb 2017cs.LGcited by 21

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Abstract (arXiv)

Recent advances in one-shot learning have produced models that can learn from a handful of labeled examples, for passive classification and regression tasks. This paper combines reinforcement learning with one-shot learning, allowing the model to decide, during classification, which examples are worth labeling. We introduce a classification task in which a stream of images are presented and, on each time step, a decision must be made to either predict a label or pay to receive the correct label. We present a recurrent neural network based action-value function, and demonstrate its ability to learn how and when to request labels. Through the choice of reward function, the model can achieve a higher prediction accuracy than a similar model on a purely supervised task, or trade prediction accuracy for fewer label requests.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Feb 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 7; 2023: 3; 2024: 2; 2025: 0; 2026: 2.

21 papers.

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  1. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  2. 2021Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approachcited by 34
  3. 2022Active Learning Helps Pretrained Models Learn the Intended Taskcited by 33
  4. 2021Improving Contrastive Learning on Imbalanced Seed Data via Open-World Samplingcited by 30
  5. 2023Active learning for data streams: a surveycited by 29
  6. 2021On Data Efficiency of Meta-learningcited by 16
  7. 2021Towards General and Efficient Active Learningcited by 6
  8. 2022Active learning using adaptable task-based prioritisationcited by 6
  9. 2021A Survey on Active Deep Learning: From Model-driven to Data-drivencited by 4
  10. 2021MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learningcited by 4
  11. 2024Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantificationcited by 4
  12. 2021ImitAL: Learning Active Learning Strategies from Synthetic Datacited by 3
9 more
  1. 2022Budget-aware Few-shot Learning via Graph Convolutional Networkcited by 3
  2. 2022Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Imagescited by 3
  3. 2022Deep reinforced active learning for multi-class image classificationcited by 3
  4. 2023Guided Transfer Learningcited by 2
  5. 2024Active Learning of Deep Neural Networks via Gradient-Free Cutting Planescited by 1
  6. 2022Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Datacited by 0
  7. 2023MyriadAL: Active Few Shot Learning for Histopathologycited by 0
  8. 2026Adaptive Active Learning for Regression via Reinforcement Learningcited by 0
  9. 2026Label What Matters: Modality-Balanced and Difficulty-Aware Multimodal Active Learningcited by 0