  Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing
the Few-Shot Learning (FSL) problem across different domains has attracted
rising attention. The core challenge of CD-FSL lies in the domain gap between
the source and novel target datasets. Though many attempts have been made for
CD-FSL without any target data during model training, the huge domain gap makes
it still hard for existing CD-FSL methods to achieve very satisfactory results.
Alternatively, learning CD-FSL models with few labeled target domain data which
is more realistic and promising is advocated in previous
work~\cite{fu2021meta}. Thus, in this paper, we stick to this setting and
technically contribute a novel Multi-Expert Domain Decompositional Network
(ME-D2N). Concretely, to solve the data imbalance problem between the source
data with sufficient examples and the auxiliary target data with limited
examples, we build our model under the umbrella of multi-expert learning. Two
teacher models which can be considered to be experts in their corresponding
domain are first trained on the source and the auxiliary target sets,
respectively. Then, the knowledge distillation technique is introduced to
transfer the knowledge from two teachers to a unified student model. Taking a
step further, to help our student model learn knowledge from different domain
teachers simultaneously, we further present a novel domain decomposition module
that learns to decompose the student model into two domain-related sub parts.
This is achieved by a novel domain-specific gate that learns to assign each
filter to only one specific domain in a learnable way. Extensive experiments
demonstrate the effectiveness of our method. Codes and models are available at
https://github.com/lovelyqian/ME-D2N_for_CDFSL.
