Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unraveling Meta-Learning: Understanding Feature Representations for
  Few-Shot Tasks   Meta-learning algorithms produce feature extractors which achieve
state-of-the-art performance on few-shot classification. While the literature
is rich with meta-learning methods, little is known about why the resulting
feature extractors perform so well. We develop a better understanding of the
underlying mechanics of meta-learning and the difference between models trained
using meta-learning and models which are trained classically. In doing so, we
introduce and verify several hypotheses for why meta-learned models perform
better. Furthermore, we develop a regularizer which boosts the performance of
standard training routines for few-shot classification. In many cases, our
routine outperforms meta-learning while simultaneously running an order of
magnitude faster.

4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom