  We propose to study the problem of few-shot learning with the prism of
inference on a partially observed graphical model, constructed from a
collection of input images whose label can be either observed or not. By
assimilating generic message-passing inference algorithms with their
neural-network counterparts, we define a graph neural network architecture that
generalizes several of the recently proposed few-shot learning models. Besides
providing improved numerical performance, our framework is easily extended to
variants of few-shot learning, such as semi-supervised or active learning,
demonstrating the ability of graph-based models to operate well on 'relational'
tasks.
