  Few shot learning aims to solve the data scarcity problem. If there is a
domain shift between the test set and the training set, their performance will
decrease a lot. This setting is called Cross-domain few-shot learning. However,
this is very challenging because the target domain is unseen during training.
Thus we propose a new setting some unlabelled data from the target domain is
provided, which can bridge the gap between the source domain and the target
domain. A benchmark for this setting is constructed using DomainNet
\cite{peng2018oment}. We come up with a self-supervised learning method to
fully utilize the knowledge in the labeled training set and the unlabelled set.
Extensive experiments show that our methods outperforms several baseline
methods by a large margin. We also carefully design an episodic training
pipeline which yields a significant performance boost.
