Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning. (arXiv:2207.13801v1 [cs.LG])

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 …

arxiv learning lg meta meta-learning scoring