  The supervised learning paradigm is limited by the cost - and sometimes the
impracticality - of data collection and labeling in multiple domains.
Self-supervised learning, a paradigm which exploits the structure of unlabeled
data to create learning problems that can be solved with standard supervised
approaches, has shown great promise as a pretraining or feature learning
approach in fields like computer vision and time series processing. In this
work, we present self-supervision strategies that can be used to learn
informative representations from multivariate time series. One successful
approach relies on predicting whether time windows are sampled from the same
temporal context or not. As demonstrated on a clinically relevant task (sleep
scoring) and with two electroencephalography datasets, our approach outperforms
a purely supervised approach in low data regimes, while capturing important
physiological information without any access to labels.
