Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators   Although few-shot learning research has advanced rapidly with the help of
meta-learning, its practical usefulness is still limited because most of them
assumed that all meta-training and meta-testing examples came from a single
domain. We propose a simple but effective way for few-shot classification in
which a task distribution spans multiple domains including ones never seen
during meta-training. The key idea is to build a pool of models to cover this
wide task distribution and learn to select the best one for a particular task
through cross-domain meta-learning. All models in the pool share a base network
while each model has a separate modulator to refine the base network in its own
way. This framework allows the pool to have representational diversity without
losing beneficial domain-invariant features. We verify the effectiveness of the
proposed algorithm through experiments on various datasets across diverse
domains.

14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat