Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Select Base Classes for Few-shot Classification   Few-shot learning has attracted intensive research attention in recent years.
Many methods have been proposed to generalize a model learned from provided
base classes to novel classes, but no previous work studies how to select base
classes, or even whether different base classes will result in different
generalization performance of the learned model. In this paper, we utilize a
simple yet effective measure, the Similarity Ratio, as an indicator for the
generalization performance of a few-shot model. We then formulate the base
class selection problem as a submodular optimization problem over Similarity
Ratio. We further provide theoretical analysis on the optimization lower bound
of different optimization methods, which could be used to identify the most
appropriate algorithm for different experimental settings. The extensive
experiments on ImageNet, Caltech256 and CUB-200-2011 demonstrate that our
proposed method is effective in selecting a better base dataset.

22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con