  Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel
classes with few examples. Recently, lots of methods have been proposed from
the perspective of meta-learning and representation learning. However, few
works focus on the interpretability of FSL decision process. In this paper, we
take a step towards the interpretable FSL by proposing a novel meta-learning
based decision tree framework, namely, MetaDT. In particular, the FSL
interpretability is achieved from two aspects, i.e., a concept aspect and a
visual aspect. On the concept aspect, we first introduce a tree-like concept
hierarchy as FSL prior. Then, resorting to the prior, we split each few-shot
task to a set of subtasks with different concept levels and then perform class
prediction via a model of decision tree. The advantage of such design is that a
sequence of high-level concept decisions that lead up to a final class
prediction can be obtained, which clarifies the FSL decision process. On the
visual aspect, a set of subtask-specific classifiers with visual attention
mechanism is designed to perform decision at each node of the decision tree. As
a result, a subtask-specific heatmap visualization can be obtained to achieve
the decision interpretability of each tree node. At last, to alleviate the data
scarcity issue of FSL, we regard the prior of concept hierarchy as an
undirected graph, and then design a graph convolution-based decision tree
inference network as our meta-learner to infer parameters of the decision tree.
Extensive experiments on performance comparison and interpretability analysis
show superiority of our MetaDT.
