通过重要性感知的多分辨率选择，提升时间序列自监督学习效率
Efficient Hierarchical Contrastive Self-supervising Learning for Time Series Classification via Importance-aware Resolution Selection
- 根据重要性动态选择训练分辨率，减少冗余计算
- 在长序列上训练速度提升显著，分类性能保持不变
- 适合大规模时间序列数据，尤其对算力有限场景友好
近期自监督学习（SSL）框架在降低时间序列数据标注依赖方面取得显著进展。基于层次对比学习的框架通过在多分辨率下对比数据嵌入来学习表示，表现出更强的泛化能力。然而，当时间序列长度较长时，其计算成本远高于其他框架。本文提出一种高效训练方法：受各分辨率嵌入高度相关的启发，设计重要性感知的分辨率选择训练框架，以降低计算开销。实验表明，该方法在大量时间序列分类任务中显著缩短训练时间，同时保持原有模型的性能表现。代码已公开于 https://github.com/KEEBVIN/IARS
原文摘要 · Abstract (English)
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
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