Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Few-Shot Classification with Feature Map Reconstruction Networks   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.

17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat