# Semi-Supervised and Active Few-Shot Learning with Prototypical Networks

arXiv: https://arxiv.org/abs/1711.10856 · HTML: https://mlsanity.com/paper/1711.10856/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 8 of 8)

- [On Data Efficiency of Meta-learning](https://mlsanity.com/paper/2102.00127/) (2021)
- [Active Few-Shot Learning with FASL](https://mlsanity.com/paper/2204.09347/) (2022)
- [Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks](https://mlsanity.com/paper/2106.13551/) (2021)
- [On the cross-lingual transferability of multilingual prototypical models across NLU tasks](https://mlsanity.com/paper/2207.09157/) (2022)
- [Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks](https://mlsanity.com/paper/2406.06079/) (2024)
- [Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2210.06120/) (2022)
- [Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining](https://mlsanity.com/paper/2102.08074/) (2021)
- [Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings](https://mlsanity.com/paper/2209.11481/) (2022)

## Abstract (arXiv)

We consider the problem of semi-supervised few-shot classification where a classifier needs to adapt to new tasks using a few labeled examples and (potentially many) unlabeled examples. We propose a clustering approach to the problem. The features extracted with Prototypical Networks are clustered using K-means with the few labeled examples guiding the clustering process. We note that in many real-world applications the adaptation performance can be significantly improved by requesting the few labels through user feedback. We demonstrate good performance of the active adaptation strategy using image data.