  In this paper, we propose Domain Agnostic Meta Score-based Learning (DAMSL),
a novel, versatile and highly effective solution that delivers significant
out-performance over state-of-the-art methods for cross-domain few-shot
learning. We identify key problems in previous meta-learning methods
over-fitting to the source domain, and previous transfer-learning methods
under-utilizing the structure of the support set. The core idea behind our
method is that instead of directly using the scores from a fine-tuned feature
encoder, we use these scores to create input coordinates for a domain agnostic
metric space. A graph neural network is applied to learn an embedding and
relation function over these coordinates to process all information contained
in the score distribution of the support set. We test our model on both
established CD-FSL benchmarks and new domains and show that our method
overcomes the limitations of previous meta-learning and transfer-learning
methods to deliver substantial improvements in accuracy across both smaller and
larger domain shifts.
