Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Regression Networks for Meta-Learning Few-Shot Classification   We propose regression networks for the problem of few-shot classification,
where a classifier must generalize to new classes not seen in the training set,
given only a small number of examples of each class. In high dimensional
embedding spaces the direction of data generally contains richer information
than magnitude. Next to this, state-of-the-art few-shot metric methods that
compare distances with aggregated class representations, have shown superior
performance. Combining these two insights, we propose to meta-learn
classification of embedded points by regressing the closest approximation in
every class subspace while using the regression error as a distance metric.
Similarly to recent approaches for few-shot learning, regression networks
reflect a simple inductive bias that is beneficial in this limited-data regime
and they achieve excellent results, especially when more aggregate class
representations can be formed with multiple shots.

2
The categories of this paper are: math
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom