  While many deep learning methods have seen significant success in tackling
the problem of domain adaptation and few-shot learning separately, far fewer
methods are able to jointly tackle both problems in Cross-Domain Few-Shot
Learning (CD-FSL). This problem is exacerbated under sharp domain shifts that
typify common computer vision applications. In this paper, we present a novel,
flexible and effective method to address the CD-FSL problem. Our method, called
Score-based Meta Transfer-Learning (SB-MTL), combines transfer-learning and
meta-learning by using a MAML-optimized feature encoder and a score-based Graph
Neural Network. First, we have a feature encoder with specific layers designed
to be fine-tuned. To do so, we apply a first-order MAML algorithm to find good
initializations. Second, instead of directly taking the classification scores
after fine-tuning, we interpret the scores as coordinates by mapping the
pre-softmax classification scores onto a metric space. Subsequently, we apply a
Graph Neural Network to propagate label information from the support set to the
query set in our score-based metric space. We test our model on the Broader
Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, which includes a
range of target domains with highly varying dissimilarity to the miniImagenet
source domain. We observe significant improvements in accuracy across 5, 20 and
50 shot, and on the four target domains. In terms of average accuracy, our
model outperforms previous transfer-learning methods by 5.93% and previous
meta-learning methods by 14.28%.
