  We propose a meta-learning algorithm utilizing a linear transformer that
carries out null-space projection of neural network outputs. The main idea is
to construct an alternative classification space such that the error signals
during few-shot learning are quickly zero-forced on that space so that reliable
classification on low data is possible. The final decision on a query is
obtained utilizing a null-space-projected distance measure between the network
output and reference vectors, both of which have been trained in the initial
learning phase. Among the known methods with a given model size, our
meta-learner achieves the best or near-best image classification accuracies
with Omniglot and miniImageNet datasets.
