  The goal of few-shot classification is to learn a model that can classify
novel classes using only a few training examples. Despite the promising results
shown by existing meta-learning algorithms in solving the few-shot
classification problem, there still remains an important challenge: how to
generalize to unseen domains while meta-learning on multiple seen domains? In
this paper, we propose an optimization-based meta-learning method, called
Combining Domain-Specific Meta-Learners (CosML), that addresses the
cross-domain few-shot classification problem. CosML first trains a set of
meta-learners, one for each training domain, to learn prior knowledge (i.e.,
meta-parameters) specific to each domain. The domain-specific meta-learners are
then combined in the \emph{parameter space}, by taking a weighted average of
their meta-parameters, which is used as the initialization parameters of a task
network that is quickly adapted to novel few-shot classification tasks in an
unseen domain. Our experiments show that CosML outperforms a range of
state-of-the-art methods and achieves strong cross-domain generalization
ability.
