  The ability to learn from a small number of examples has been a difficult
problem in machine learning since its inception. While methods have succeeded
with large amounts of training data, research has been underway in how to
accomplish similar performance with fewer examples, known as one-shot or more
generally few-shot learning. This technique has been shown to have promising
performance, but in practice requires fixed-size inputs making it impractical
for production systems where class sizes can vary. This impedes training and
the final utility of few-shot learning systems. This paper describes an
approach to constructing and training a network that can handle arbitrary
example sizes dynamically as the system is used.
