We unify recent neural approaches to one-shot learning with older ideas of
associative memory in a model for metalearning. Our model learns jointly to
represent data and to bind class labels to representations in a single shot. It
builds representations via slow weights, learned across tasks through SGD,
while fast weights constructed by a Hebbian learning rule implement one-shot
binding for each new task. On the Omniglot, Mini-ImageNet, and Penn Treebank
one-shot learning benchmarks, our model achieves state-of-the-art results.