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.

提出了一种新的基于对比损失的无监督表示学习框架，并通过利用数据增广方案生成新样本来优化损失函数，实现混合样本的预测任务，以利用软目标进行学习。实验结果表明本方法在单变量和多变量时间序列上的表现优于其他表示学习方法，并展示了它对于临床时间序列的传递学习的好处。