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 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.