  In this paper we reformulate few-shot classification as a reconstruction
problem in latent space. The ability of the network to reconstruct a query
feature map from support features of a given class predicts membership of the
query in that class. We introduce a novel mechanism for few-shot classification
by regressing directly from support features to query features in closed form,
without introducing any new modules or large-scale learnable parameters. The
resulting Feature Map Reconstruction Networks are both more performant and
computationally efficient than previous approaches. We demonstrate consistent
and substantial accuracy gains on four fine-grained benchmarks with varying
neural architectures. Our model is also competitive on the non-fine-grained
mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles.
