  A recent study finds that existing few-shot learning methods, trained on the
source domain, fail to generalize to the novel target domain when a domain gap
is observed. This motivates the task of Cross-Domain Few-Shot Learning
(CD-FSL). In this paper, we realize that the labeled target data in CD-FSL has
not been leveraged in any way to help the learning process. Thus, we advocate
utilizing few labeled target data to guide the model learning. Technically, a
novel meta-FDMixup network is proposed. We tackle this problem mainly from two
aspects. Firstly, to utilize the source and the newly introduced target data of
two different class sets, a mixup module is re-proposed and integrated into the
meta-learning mechanism. Secondly, a novel disentangle module together with a
domain classifier is proposed to extract the disentangled domain-irrelevant and
domain-specific features. These two modules together enable our model to narrow
the domain gap thus generalizing well to the target datasets. Additionally, a
detailed feasibility and pilot study is conducted to reflect the intuitive
understanding of CD-FSL under our new setting. Experimental results show the
effectiveness of our new setting and the proposed method. Codes and models are
available at https://github.com/lovelyqian/Meta-FDMixup.
