  Given sufficient training data on the source domain, cross-domain few-shot
learning (CD-FSL) aims at recognizing new classes with a small number of
labeled examples on the target domain. The key to addressing CD-FSL is to
narrow the domain gap and transferring knowledge of a network trained on the
source domain to the target domain. To help knowledge transfer, this paper
introduces an intermediate domain generated by mixing images in the source and
the target domain. Specifically, to generate the optimal intermediate domain
for different target data, we propose a novel target guided dynamic mixup
(TGDM) framework that leverages the target data to guide the generation of
mixed images via dynamic mixup. The proposed TGDM framework contains a Mixup-3T
network for learning classifiers and a dynamic ratio generation network (DRGN)
for learning the optimal mix ratio. To better transfer the knowledge, the
proposed Mixup-3T network contains three branches with shared parameters for
classifying classes in the source domain, target domain, and intermediate
domain. To generate the optimal intermediate domain, the DRGN learns to
generate an optimal mix ratio according to the performance on auxiliary target
data. Then, the whole TGDM framework is trained via bi-level meta-learning so
that TGDM can rectify itself to achieve optimal performance on target data.
Extensive experimental results on several benchmark datasets verify the
effectiveness of our method.
