  Few-shot learning aims at recognizing new instances from classes with limited
samples. This challenging task is usually alleviated by performing
meta-learning on similar tasks. However, the resulting models are black-boxes.
There has been growing concerns about deploying black-box machine learning
models and FSL is not an exception in this regard. In this paper, we propose a
method for FSL based on a set of human-interpretable concepts. It constructs a
set of metric spaces associated with the concepts and classifies samples of
novel classes by aggregating concept-specific decisions. The proposed method
does not require concept annotations for query samples. This interpretable
method achieved results on a par with six previously state-of-the-art black-box
FSL methods on the CUB fine-grained bird classification dataset.
