  Few-shot learning (FSL) is a challenging learning problem in which only a few
samples are available for each class. Decision interpretation is more important
in few-shot classification since there is a greater chance of error than in
traditional classification. However, most of the previous FSL methods are
black-box models. In this paper, we propose an inherently interpretable model
for FSL based on human-friendly attributes. Moreover, we propose an online
attribute selection mechanism that can effectively filter out irrelevant
attributes in each episode. The attribute selection mechanism improves the
accuracy and helps with interpretability by reducing the number of participated
attributes in each episode. We propose a mechanism that automatically detects
the episodes where the pool of human-friendly attributes are not adequate, and
compensates by engaging learned unknown attributes. We demonstrate that the
proposed method achieves results on par with black-box few-shot-learning models
on four widely used datasets.
