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In this paper, we propose Domain Agnostic Meta Score-based Learning (DAMSL),a novel, versatile and highly effective solution that delivers significantout-performance over state-of-the-art methods for cross-domain few-shotlearning. We identify key problems in previous meta-learning methodsover-fitting to the source domain, and previous transfer-learning methodsunder-utilizing the structure of the support set. The core idea behind ourmethod is that instead of directly using the scores from a fine-tuned featureencoder, we use these scores to create input coordinates for a domain agnosticmetric space. A graph neural network is applied to learn an embedding andrelation function over these coordinates to process all information containedin the score distribution of the support set. We test our model on bothestablished CD-FSL benchmarks and new domains and show that our methodovercomes the limitations of previous meta-learning and transfer-learningmethods to deliver substantial improvements in accuracy across both smaller andlarger domain shifts.

DAMSL: Domain Agnostic Meta Score-based Learning