  Existing few-shot learning (FSL) methods make the implicit assumption that
the few target class samples are from the same domain as the source class
samples. However, in practice this assumption is often invalid -- the target
classes could come from a different domain. This poses an additional challenge
of domain adaptation (DA) with few training samples. In this paper, the problem
of domain-adaptive few-shot learning (DA-FSL) is tackled, which requires
solving FSL and DA in a unified framework. To this end, we propose a novel
domain-adversarial prototypical network (DAPN) model. It is designed to address
a specific challenge in DA-FSL: the DA objective means that the source and
target data distributions need to be aligned, typically through a shared
domain-adaptive feature embedding space; but the FSL objective dictates that
the target domain per class distribution must be different from that of any
source domain class, meaning aligning the distributions across domains may harm
the FSL performance. How to achieve global domain distribution alignment whilst
maintaining source/target per-class discriminativeness thus becomes the key.
Our solution is to explicitly enhance the source/target per-class separation
before domain-adaptive feature embedding learning in the DAPN, in order to
alleviate the negative effect of domain alignment on FSL. Extensive experiments
show that our DAPN outperforms the state-of-the-art FSL and DA models, as well
as their na\"ive combinations. The code is available at
https://github.com/dingmyu/DAPN.
