通过重要性感知分辨率选择实现用于时间序列分类的高效分层对比自监督学习
Efficient Hierarchical Contrastive Self-supervising Learning for Time Series Classification via Importance-aware Resolution Selection
AI总结：
针对长序列下分层对比自监督学习计算开销高的问题，提出重要性感知分辨率选择训练框架，在保持时间序列分类性能的同时显著缩短训练时间。
摘要：
近来，在设计用于时间序列数据的自监督学习（SSL）框架以减少对数据标签依赖方面已有显著进展。在这些工作中，基于分层对比学习的SSL框架通过在多个分辨率上对比数据嵌入来学习表示，已受到相当多关注。由于其能够汇集更多信息，它们在各种下游任务中表现出更好的泛化能力。然而，当时间序列数据长度非常长时，其计算成本通常显著高于其他SSL框架。本文为应对这一挑战，提出了一种训练分层对比学习模型的高效方法。受每个分辨率的数据嵌入高度相关这一事实启发，我们引入基于重要性感知分辨率选择的训练框架以降低计算成本。实验中，我们在广泛的时间序列分类性能评估中证明，所提方法显著改善训练时间，同时保持原有模型的完整性。代码可在 https://github.com/KEEBVIN/IARS 获取。
英文摘要：
Recently, there has been a significant advancement in designing Self-Supervised Learning (SSL) frameworks for time series data to reduce the dependency on data labels. Among these works, hierarchical contrastive learning-based SSL frameworks, which learn representations by contrasting data embeddings at multiple resolutions, have gained considerable attention. Due to their ability to gather more information, they exhibit better generalization in various downstream tasks. However, when the time series data length is significant long, the computational cost is often significantly higher than that of other SSL frameworks. In this paper, to address this challenge, we propose an efficient way to train hierarchical contrastive learning models. Inspired by the fact that each resolution's data embedding is highly dependent, we introduce importance-aware resolution selection based training framework to reduce the computational cost. In the experiment, we demonstrate that the proposed method significantly improves training time while preserving the original model's integrity in extensive time series classification performance evaluations. Our code could be found here, https://github.com/KEEBVIN/IARS
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。
