  In this paper, we tackle the new Cross-Domain Few-Shot Learning benchmark
proposed by the CVPR 2020 Challenge. To this end, we build upon
state-of-the-art methods in domain adaptation and few-shot learning to create a
system that can be trained to perform both tasks. Inspired by the need to
create models designed to be fine-tuned, we explore the integration of
transfer-learning (fine-tuning) with meta-learning algorithms, to train a
network that has specific layers that are designed to be adapted at a later
fine-tuning stage. To do so, we modify the episodic training process to include
a first-order MAML-based meta-learning algorithm, and use a Graph Neural
Network model as the subsequent meta-learning module. We find that our proposed
method helps to boost accuracy significantly, especially when combined with
data augmentation. In our final results, we combine the novel method with the
baseline method in a simple ensemble, and achieve an average accuracy of 73.78%
on the benchmark. This is a 6.51% improvement over existing benchmarks that
were trained solely on miniImagenet.
