  To recognize the unseen classes with only few samples, few-shot learning
(FSL) uses prior knowledge learned from the seen classes. A major challenge for
FSL is that the distribution of the unseen classes is different from that of
those seen, resulting in poor generalization even when a model is meta-trained
on the seen classes. This class-difference-caused distribution shift can be
considered as a special case of domain shift. In this paper, for the first
time, we propose a domain adaptation prototypical network with attention
(DAPNA) to explicitly tackle such a domain shift problem in a meta-learning
framework. Specifically, armed with a set transformer based attention module,
we construct each episode with two sub-episodes without class overlap on the
seen classes to simulate the domain shift between the seen and unseen classes.
To align the feature distributions of the two sub-episodes with limited
training samples, a feature transfer network is employed together with a margin
disparity discrepancy (MDD) loss. Importantly, theoretical analysis is provided
to give the learning bound of our DAPNA. Extensive experiments show that our
DAPNA outperforms the state-of-the-art FSL alternatives, often by significant
margins.
