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.

翻译：小样本学习是一项具有挑战性的任务，旨在通过少量样本识别新类别。近年来，基于元学习和表示学习的方法层出不穷，但鲜有研究关注小样本学习决策过程的可解释性。本文提出了一种基于元学习的决策树框架MetaDT，向可解释小样本学习迈出关键一步。具体而言，我们从概念层面和视觉层面两个维度实现可解释性。在概念层面，首先引入树状概念层次结构作为小样本学习的先验知识；借助该先验，将每个小样本任务分解为不同概念层级的子任务集合，并通过决策树模型进行类别预测。这种设计的优势在于，能够获得最终类别预测前的一系列高层概念决策序列，从而阐明小样本学习的决策过程。在视觉层面，设计了配备视觉注意力机制的子任务分类器，在决策树各节点执行决策，从而获得每个树节点的子任务热力图可视化，实现各节点的决策可解释性。最后，为缓解小样本学习的数据稀缺问题，我们将概念层次结构的先验知识建模为无向图，并设计基于图卷积的决策树推理网络作为元学习器，以推断决策树参数。在性能对比与可解释性分析上的大量实验表明，MetaDT具有显著优势。