Semi-Supervised Few-Shot Learning with Prototypical Networks

We consider the problem of semi-supervised few-shot classification (when the few labeled samples are accompanied with unlabeled data) and show how to adapt the Prototypical Networks [10] to this problem. We first show that using larger and better regularized prototypical networks can improve the classification accuracy. We then show further improvements by making use of unlabeled data.