  Cross-domain few-shot classification induces a much more challenging problem
than its in-domain counterpart due to the existence of domain shifts between
the training and test tasks. In this paper, we develop a novel Adaptive
Parametric Prototype Learning (APPL) method under the meta-learning convention
for cross-domain few-shot classification. Different from existing prototypical
few-shot methods that use the averages of support instances to calculate the
class prototypes, we propose to learn class prototypes from the concatenated
features of the support set in a parametric fashion and meta-learn the model by
enforcing prototype-based regularization on the query set. In addition, we
fine-tune the model in the target domain in a transductive manner using a
weighted-moving-average self-training approach on the query instances. We
conduct experiments on multiple cross-domain few-shot benchmark datasets. The
empirical results demonstrate that APPL yields superior performance than many
state-of-the-art cross-domain few-shot learning methods.
