  Contrastive learning, a self-supervised learning method that can learn
representations from unlabeled data, has been developed promisingly. Many
methods of contrastive learning depend on data augmentation techniques, which
generate different views from the original signal. However, tuning policies and
hyper-parameters for more effective data augmentation methods in contrastive
learning is often time and resource-consuming. Researchers have designed
approaches to automatically generate new views for some input signals,
especially on the image data. But the view-learning method is not well
developed for time-series data. In this work, we propose a simple but effective
module for automating view generation for time-series data in contrastive
learning, named learning views for time-series data (LEAVES). The proposed
module learns the hyper-parameters for augmentations using adversarial training
in contrastive learning. We validate the effectiveness of the proposed method
using multiple time-series datasets. The experiments demonstrate that the
proposed method is more effective in finding reasonable views and performs
downstream tasks better than the baselines, including manually tuned
augmentation-based contrastive learning methods and SOTA methods.
