Time series classification is a critical task in various domains, such as
finance, healthcare, and sensor data analysis. Unsupervised contrastive
learning has garnered significant interest in learning effective
representations from time series data with limited labels. The prevalent
approach in existing contrastive learning methods consists of two separate
stages: pre-training the encoder on unlabeled datasets and fine-tuning the
well-trained model on a small-scale labeled dataset. However, such two-stage
approaches suffer from several shortcomings, such as the inability of
unsupervised pre-training contrastive loss to directly affect downstream
fine-tuning classifiers, and the lack of exploiting the classification loss
which is guided by valuable ground truth. In this paper, we propose an
end-to-end model called SLOTS (Semi-supervised Learning fOr Time
clasSification). SLOTS receives semi-labeled datasets, comprising a large
number of unlabeled samples and a small proportion of labeled samples, and maps
them to an embedding space through an encoder. We calculate not only the
unsupervised contrastive loss but also measure the supervised contrastive loss
on the samples with ground truth. The learned embeddings are fed into a
classifier, and the classification loss is calculated using the available true
labels. The unsupervised, supervised contrastive losses and classification loss
are jointly used to optimize the encoder and classifier. We evaluate SLOTS by
comparing it with ten state-of-the-art methods across five datasets. The
results demonstrate that SLOTS is a simple yet effective framework. When
compared to the two-stage framework, our end-to-end SLOTS utilizes the same
input data, consumes a similar computational cost, but delivers significantly
improved performance. We release code and datasets at
this https URL

本文提出了一个称为 SLOTS（半监督学习用于时间分类）的端到端模型，该模型利用无标签样本和少量有标签样本来训练编码器和分类器，并使用无监督对比损失、有监督对比损失和分类损失来优化模型。通过与十种最先进的方法在五个数据集上的比较，实验结果表明 SLOTS 是一个简单而有效的框架，相较于两阶段的方法，SLOTS 在相似的计算成本下，能够以显著提升的性能使用相同的输入数据。