LEAVES：对比学习中时间序列生物行为数据的学习视图
LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning
AI总结：
针对对比学习中时间序列生物行为数据视图生成参数优化耗时且资源密集的问题，提出自动视图生成模块LEAVES，通过对抗训练学习增强超参数，仅需约20个参数即可在SimCLR和BYOL框架上取得优于基线的性能，适用于大规模实时医疗应用。
摘要：
对比学习已被作为一种有前景的自监督学习方法，用于从无标签数据中提取有意义的表示。这些方法大多利用数据增强技术从原始输入中创建多样化的视图。然而，在对比学习框架中优化增强及其参数以生成更有效的视图，通常既耗费资源又耗时。尽管已有几种策略被提出用于在计算机视觉中自动生成新视图，但在其他领域（如时间序列生物行为数据）的研究仍然有限。在本文中，我们引入了一个简单而强大的模块，用于在应用于时间序列生物行为数据的对比学习框架中自动生成视图，这对于现代医疗保健至关重要，该模块被称为时间序列数据学习视图（LEAVES）。该提出的模块采用对抗训练来学习对比学习框架内的增强超参数。我们使用两个著名的对比学习框架，即SimCLR和BYOL，在多个时间序列数据集上评估了我们方法的有效性。在四个不同的生物行为数据集上，LEAVES仅需大约20个可学习参数——远远少于像ViewMaker这样的框架所需的约58万个参数，ViewMaker是先前在对比学习中提出的一种对抗训练的卷积模块，同时实现了与现有基线方法相比具有竞争力且通常更优的性能。关键的是，这些效率的提升是在无需大量手动超参数调整的情况下获得的，这使得LEAVES特别适合既要求准确性又要求实用性的大规模或实时医疗保健应用。
英文摘要：
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, research in other domains, such as time-series biobehavioral data, remains limited. In this paper, we introduce a simple yet powerful module for automatic view generation in contrastive 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 approximately 20 learnable parameters -- dramatically fewer than the about 580k parameters demanded by frameworks like ViewMaker, a 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 hyperparameter tuning, which makes LEAVES particularly suitable for large-scale or real-time healthcare applications that demand both accuracy and practicality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
