  The lack of labeled data is a key challenge for learning useful
representation from time series data. However, an unsupervised representation
framework that is capable of producing high quality representations could be of
great value. It is key to enabling transfer learning, which is especially
beneficial for medical applications, where there is an abundance of data but
labeling is costly and time consuming. We propose an unsupervised contrastive
learning framework that is motivated from the perspective of label smoothing.
The proposed approach uses a novel contrastive loss that naturally exploits a
data augmentation scheme in which new samples are generated by mixing two data
samples with a mixing component. The task in the proposed framework is to
predict the mixing component, which is utilized as soft targets in the loss
function. Experiments demonstrate the framework's superior performance compared
to other representation learning approaches on both univariate and multivariate
time series and illustrate its benefits for transfer learning for clinical time
series.
