Hierarchical Meta-Learning with Hyper-Tasks for Few-Shot Learning

Yunchuan Guan, Yu Liu, Ke Zhou, Junyuan Huang

Abstract: Meta-learning excels in few-shot learning by extracting shared knowledge from the observed tasks. However, it needs the tasks to adhere to the i.i.d. constraint, which is challenging to achieve due to complex task relationships between data content. Current methods that create tasks in a one-dimensional structure and use meta-learning to learn all tasks flatly struggle with extracting shared knowledge from tasks with overlapping concepts. To address this issue, we propose further constructing tasks from the same environment into hyper-tasks. Since the distributions of hyper-tasks and tasks in a hyper-task can both be approximated as i.i.d. due to further summarization, the meta-learning algorithm can capture shared knowledge more efficiently. Based on the hyper-task, we propose a hierarchical meta-learning paradigm to meta-learn the meta-learning algorithm. The paradigm builds a customized meta-learner for each hyper-task, which makes meta-learners more flexible and expressive. We apply the paradigm to three classic meta-learning algorithms and conduct extensive experiments on public datasets, which confirm the superiority of hierarchical meta-learning in the few-shot learning setting. The code is released at https://github.com/tuantuange/H-meta-learning.