论文标题
通过自我监督的元学习进行睡眠评分概括
Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning
论文作者
论文摘要
在这项工作中，我们介绍了一种新型的元学习方法，用于基于自学学习的学习来进行睡眠评分。我们的方法旨在构建可以概括不同患者和记录设施的睡眠评分模型，但不需要进一步适应目标数据。为了实现这一目标，我们通过合并自我监督的学习（SSL）阶段并将其称为S2MAML，在模型不可知的元学习（MAML）框架之上构建方法。我们表明，S2MAML可以显着胜过MAML。性能的增长来自SSL阶段，我们以通用伪任务为基础，该任务限制了训练数据集中存在的特定主题模式。我们表明，S2MAML在SC，ST，ISRUC，UCD和CAP数据集上胜过标准的监督学习和MAML。
In this work we introduce a novel meta-learning method for sleep scoring based on self-supervised learning. Our approach aims at building models for sleep scoring that can generalize across different patients and recording facilities, but do not require a further adaptation step to the target data. Towards this goal, we build our method on top of the Model Agnostic Meta-Learning (MAML) framework by incorporating a self-supervised learning (SSL) stage, and call it S2MAML. We show that S2MAML can significantly outperform MAML. The gain in performance comes from the SSL stage, which we base on a general purpose pseudo-task that limits the overfitting to the subject-specific patterns present in the training dataset. We show that S2MAML outperforms standard supervised learning and MAML on the SC, ST, ISRUC, UCD and CAP datasets.
