  Few-shot models have become a popular topic of research in the past years.
They offer the possibility to determine class belongings for unseen examples
using just a handful of examples for each class. Such models are trained on a
wide range of classes and their respective examples, learning a decision metric
in the process. Types of few-shot models include matching networks and
prototypical networks. We show a new way of training prototypical few-shot
models for just a single class. These models have the ability to predict the
likelihood of an unseen query belonging to a group of examples without any
given counterexamples. The difficulty here lies in the fact that no relative
distance to other classes can be calculated via softmax. We solve this problem
by introducing a "null class" centered around zero, and enforcing centering
with batch normalization. Trained on the commonly used Omniglot data set, we
obtain a classification accuracy of .98 on the matched test set, and of .8 on
unmatched MNIST data. On the more complex MiniImageNet data set, test accuracy
is .8. In addition, we propose a novel Gaussian layer for distance calculation
in a prototypical network, which takes the support examples' distribution
rather than just their centroid into account. This extension shows promising
results when a higher number of support examples is available.
