Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-domain Few-shot Learning with Task-specific Adapters   In this paper, we look at the problem of cross-domain few-shot classification
that aims to learn a classifier from previously unseen classes and domains with
few labeled samples. Recent approaches broadly solve this problem by
parameterizing their few-shot classifiers with task-agnostic and task-specific
weights where the former is typically learned on a large training set and the
latter is dynamically predicted through an auxiliary network conditioned on a
small support set. In this work, we focus on the estimation of the latter, and
propose to learn task-specific weights from scratch directly on a small support
set, in contrast to dynamically estimating them. In particular, through
systematic analysis, we show that task-specific weights through parametric
adapters in matrix form with residual connections to multiple intermediate
layers of a backbone network significantly improves the performance of the
state-of-the-art models in the Meta-Dataset benchmark with minor additional
cost.

13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int