MetaDT：具有类别层次结构的元决策树用于可解释的小样本学习
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning
AI总结：
本文提出MetaDT，一种基于元学习的决策树框架，利用类别层次结构先验和视觉注意力机制，实现小样本学习决策过程的可解释性，并通过图卷积推理网络缓解数据稀缺问题。
摘要：
小样本学习（Few-Shot Learning, FSL）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，从元学习和表示学习的角度提出了许多方法。然而，很少有工作关注FSL决策过程的可解释性。在本文中，我们通过提出一种新颖的基于元学习的决策树框架，即MetaDT，向可解释的FSL迈出了一步。具体而言，FSL的可解释性从两个方面实现，即概念方面和视觉方面。在概念方面，我们首先引入一个树状的概念层次结构作为FSL的先验知识。然后，借助该先验，我们将每个小样本任务划分为一组具有不同概念级别的子任务，并通过决策树模型进行类别预测。这种设计的优势在于，可以获得一系列导致最终类别预测的高层概念决策，从而阐明FSL的决策过程。在视觉方面，设计了一组具有视觉注意力机制的子任务特定分类器，以在决策树的每个节点执行决策。因此，可以获得子任务特定的热图可视化，以实现每个树节点的决策可解释性。最后，为了缓解FSL的数据稀缺问题，我们将概念层次的先验视为无向图，然后设计一个基于图卷积的决策树推理网络作为我们的元学习器，以推断决策树的参数。在性能比较和可解释性分析方面的大量实验表明了我们MetaDT的优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
