Hierarchically Structured Meta-learning
Huaxiu Yao · Ying WEI · Junzhou Huang · Zhenhui (Jessie) Li

Thu Jun 13th 09:35 -- 09:40 AM @ Room 103

In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks. However, a critical challenge in meta-learning is task uncertainty and heterogeneity, which can not be handled via globally sharing knowledge among tasks. In this paper, based on gradient-based meta-learning, we propose a hierarchically structured meta-learning (HSML) algorithm that explicitly tailors the transferable knowledge to different clusters of tasks. Inspired by the way human beings organize knowledge, we resort to a hierarchical task clustering structure to cluster tasks.
As a result, the proposed approach not only addresses the challenge via the knowledge customization to different clusters of tasks, but also preserves knowledge generalization among a cluster of similar tasks. To tackle the changing of task relationship, in addition, we extend the hierarchical structure to a continual learning environment. The experimental results show that our approach can achieve state-of-the-art performance in both toy-regression and few-shot image classification problems.

Author Information

Huaxiu Yao (Pennsylvania State University)
Ying WEI (Tencent AI Lab)
Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)
Zhenhui (Jessie) Li (Penn State University)

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