# Active One-shot Learning

arXiv: https://arxiv.org/abs/1702.06559 · HTML: https://mlsanity.com/paper/1702.06559/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2017.

## Built on by (most cited first, 21 of 21)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach](https://mlsanity.com/paper/2106.02968/) (2021)
- [Active Learning Helps Pretrained Models Learn the Intended Task](https://mlsanity.com/paper/2204.08491/) (2022)
- [Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling](https://mlsanity.com/paper/2111.01004/) (2021)
- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [On Data Efficiency of Meta-learning](https://mlsanity.com/paper/2102.00127/) (2021)
- [Towards General and Efficient Active Learning](https://mlsanity.com/paper/2112.07963/) (2021)
- [Active learning using adaptable task-based prioritisation](https://mlsanity.com/paper/2212.01703/) (2022)
- [A Survey on Active Deep Learning: From Model-driven to Data-driven](https://mlsanity.com/paper/2101.09933/) (2021)
- [MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.14339/) (2021)
- [Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification](https://mlsanity.com/paper/2411.07097/) (2024)
- [ImitAL: Learning Active Learning Strategies from Synthetic Data](https://mlsanity.com/paper/2108.07670/) (2021)
- [Budget-aware Few-shot Learning via Graph Convolutional Network](https://mlsanity.com/paper/2201.02304/) (2022)
- [Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images](https://mlsanity.com/paper/2201.11197/) (2022)
- [Deep reinforced active learning for multi-class image classification](https://mlsanity.com/paper/2206.13391/) (2022)
- [Guided Transfer Learning](https://mlsanity.com/paper/2303.16154/) (2023)
- [Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes](https://mlsanity.com/paper/2410.02145/) (2024)
- [Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data](https://mlsanity.com/paper/2209.14199/) (2022)
- [MyriadAL: Active Few Shot Learning for Histopathology](https://mlsanity.com/paper/2310.16161/) (2023)
- [Adaptive Active Learning for Regression via Reinforcement Learning](https://mlsanity.com/paper/2603.10435/) (2026)
- [Label What Matters: Modality-Balanced and Difficulty-Aware Multimodal Active Learning](https://mlsanity.com/paper/2603.25107/) (2026)

## 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.