针对假负样本和类别不平衡的时间序列对比学习
Time-Series Contrastive Learning against False Negatives and Class Imbalance
AI总结：
本文分析了时间序列对比学习中被忽视的假负样本和类别不平衡问题，基于SimCLR框架提出构建实例图模拟监督对比学习以减轻假负样本影响，并结合图结构和少量标签进行半监督分类以增强少数类表征，在四个真实数据集上显著提升了整体性能。
摘要：
作为表征学习的典型自监督方法，时间序列对比学习在当代研究中展现出显著进展。尽管近期的对比学习策略侧重于如何构建合适的正样本和负样本，但在本研究中，我们进行了理论分析并发现它们忽略了根本问题：基于InfoNCE损失框架中固有的假负样本和类别不平衡。因此，我们引入了基于SimCLR框架的简单修改，该修改普遍适用于参与实例判别任务的模型。通过构建实例图以促进实例间的交互学习，我们通过多实例判别任务模拟监督对比学习，减轻了假负样本的有害影响。此外，利用图结构和少量标签数据，我们执行半监督一致性分类，并增强少数类的表征能力。我们在四个真实世界时间序列数据集上将我们的方法与最流行的时间序列对比学习方法进行了比较，并展示了我们在整体性能上的显著优势。
英文摘要：
As an exemplary self-supervised approach for representation learning, time-series contrastive learning has exhibited remarkable advancements in contemporary research. While recent contrastive learning strategies have focused on how to construct appropriate positives and negatives, in this study, we conduct theoretical analysis and find they have overlooked the fundamental issues: false negatives and class imbalance inherent in the InfoNCE loss-based framework. Therefore, we introduce a straightforward modification grounded in the SimCLR framework, universally adaptable to models engaged in the instance discrimination task. By constructing instance graphs to facilitate interactive learning among instances, we emulate supervised contrastive learning via the multiple-instances discrimination task, mitigating the harmful impact of false negatives. Moreover, leveraging the graph structure and few-labeled data, we perform semi-supervised consistency classification and enhance the representative ability of minority classes. We compared our method with the most popular time-series contrastive learning methods on four real-world time-series datasets and demonstrated our significant advantages in overall performance.
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。
