LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning

Contrastive learning has been utilized as a promising self-supervised learning approach to extract meaningful representations from unlabeled data. The majority of these methods take advantage of data-augmentation techniques to create diverse views from the original input. However, optimizing augmentations and their parameters for generating more effective views in contrastive learning frameworks is often resource-intensive and time-consuming. While several strategies have been proposed for automatically generating new views in computer vision (Tamkin et al., 2020; Rusak et al., 2020), research in other domains, such as time-series biobehavioral data, remains limited. In this pa per, we introduce a simple yet powerful module for automatic view generation in con trastive learning frameworks applied to time-series biobehavioral data, which is essential for modern health care, termed learning views for time-series data (LEAVES). This proposed module employs adversarial training to learn augmentation hyperparameters within contrastive learning frameworks. We assess the efficacy of our method on multiple time series datasets using two well-known contrastive learning frameworks, namely SimCLR and BYOL. Across four diverse biobehavioral datasets, LEAVES requires only 20 learnable pa rameters—dramatically fewer than the 580,000 parameters demanded by frameworks like ViewMaker, previously proposed adversarially trained convolutional module in contrastive learning, while achieving competitive and often superior performance to existing baseline methods. Crucially, these efficiency gains are obtained without extensive manual hyper parameter tuning, which makes LEAVES particularly suitable for large-scale or real-time healthcare applications that demand both accuracy and practicality. The code of this work is available at: https://github.com/comp-well-org/LEAVES.