Few-shot Semi-Supervised Learning with Meta-Learning

Contact: Arnout Devos

keywords: deep learning, meta-learning, few-shot learning

Problem & Setting

In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Meta-learning is a successful approach for few-shot learning. A more natural extension of the few-shot learning problem is a setting where, next to the few labeled examples (shots), there are many unlabeled examples present. A real-life application would be a camera mounted on a car, where only few images of all the collected images need to be labeled to build a performant classifier. The idea of the project is to investigate state-of-the-art meta-learning algorithms in the setting of semi-supervised learning.

The first part of the project will consist mostly of a literature search on few-shot learning algorithms and their extensions to semi-supervised learning. After investigating the benefits and drawbacks of each method, we will implement some methods and experimentally compare their performance for the case of few-shot semi-supervised learning.

This semester project is targeted towards a master's student.

Required skills

  • Interest in machine learning research
  • Strong programming skills (Python & PyTorch/TensorFlow preferred)


  • Explore an active field of research in machine learning and meta-learning
  • Get hands-on experience with state-of-the-art machine learning methods and libraries

Reference work:
Meta-Learning for Semi-Supervised Few-Shot Classification [ICLR 2018]