Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combat Data Shift in Few-shot Learning with Knowledge Graph   Many few-shot learning approaches have been designed under the meta-learning
framework, which learns from a variety of learning tasks and generalizes to new
tasks. These meta-learning approaches achieve the expected performance in the
scenario where all samples are drawn from the same distributions (i.i.d.
observations). However, in real-world applications, few-shot learning paradigm
often suffers from data shift, i.e., samples in different tasks, even in the
same task, could be drawn from various data distributions. Most existing
few-shot learning approaches are not designed with the consideration of data
shift, and thus show downgraded performance when data distribution shifts.
However, it is non-trivial to address the data shift problem in few-shot
learning, due to the limited number of labeled samples in each task. Targeting
at addressing this problem, we propose a novel metric-based meta-learning
framework to extract task-specific representations and task-shared
representations with the help of knowledge graph. The data shift within/between
tasks can thus be combated by the combination of task-shared and task-specific
representations. The proposed model is evaluated on popular benchmarks and two
constructed new challenging datasets. The evaluation results demonstrate its
remarkable performance.

15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph