In order to efficiently learn with small amount of data on new tasks,
meta-learning transfers knowledge learned from previous tasks to the new ones.
However, a critical challenge in meta-learning is the task heterogeneity which
cannot be well handled by traditional globally shared meta-learning methods. In
addition, current task-specific meta-learning methods may either suffer from
hand-crafted structure design or lack the capability to capture complex
relations between tasks. In this paper, motivated by the way of knowledge
organization in knowledge bases, we propose an automated relational
meta-learning (ARML) framework that automatically extracts the cross-task
relations and constructs the meta-knowledge graph. When a new task arrives, it
can quickly find the most relevant structure and tailor the learned structure
knowledge to the meta-learner. As a result, the proposed framework not only
addresses the challenge of task heterogeneity by a learned meta-knowledge
graph, but also increases the model interpretability. We conduct extensive
experiments on 2D toy regression and few-shot image classification and the
results demonstrate the superiority of ARML over state-of-the-art baselines.

本篇论文提出一种基于知识库组织方式的自动关系元学习（ARML）框架，它可以从先前完成的任务中自动抽取跨任务关系并构建元知识图，通过学习的元知识图解决任务异质性的挑战，同时提高模型可解释性。