Hierarchical multi-task learning with self-supervised auxiliary task
Seung‐Han Lee, Taeyoung Park · Korean Journal of Applied Statistics · 2024
Multi-task learning is a popular approach in machine learning that aims to learn multiple related tasks simultaneously by sharing information across them.In this paper, we consider a hierarchical structure across multiple related tasks with a hierarchy of sub-tasks under the same main task, where representations used to solve the sub-tasks share more information through task-specific layers, globally shared layers, and locally shared layers.We thus propose the hierarchical multi-task learning with self-supervised auxiliary task (HiSS), which is a novel approach for hierarchical multi-task learning that incorporates self-supervised learning as an auxiliary task.The goal of the auxiliary task is to further extract latent information from the unlabeled data by predicting a cluster label directly derived from the data.The proposed approach is tested on the Hyodoll dataset, which consists of user information and activity logs of elderly individuals collected by AI companion robots, for predicting emergency calls based on the time of day and month.Our proposed algorithm is more efficient than other wellknown machine learning algorithms as it requires only a single model regardless of the number of tasks, and demonstrates superior performance in classification tasks using various metrics.The source codes are available at: https://github.com/seunghan96/HiSS.