面向含噪时间序列的去噪感知对比学习
Denoising-Aware Contrastive Learning for Noisy Time Series
AI总结：
针对时间序列自监督学习中噪声损害性能的问题，本文提出去噪感知对比学习DECL，通过对比学习目标缓解表示噪声并为每个样本自动选择去噪方法，实验验证了其有效性。
摘要：
时间序列自监督学习（SSL）旨在利用无标签数据进行预训练，以减轻对标签的依赖。尽管近年来取得了巨大成功，但关于时间序列中潜在噪声的讨论仍然有限，而噪声可能严重损害现有SSL方法的性能。为了缓解噪声，事实上的策略是在模型训练之前应用传统去噪方法。然而，这种预处理方法可能无法完全消除SSL中噪声的影响，原因有二：（i）时间序列中噪声类型多样，难以自动确定合适的去噪方法；（ii）将原始数据映射到潜在空间后，噪声可能被放大。在本文中，我们提出了去噪感知对比学习（DECL），该方法利用对比学习目标来缓解表示中的噪声，并为每个样本自动选择合适的去噪方法。在多个数据集上的大量实验验证了我们方法的有效性。代码已开源。
英文摘要：
Time series self-supervised learning (SSL) aims to exploit unlabeled data for pre-training to mitigate the reliance on labels. Despite the great success in recent years, there is limited discussion on the potential noise in the time series, which can severely impair the performance of existing SSL methods. To mitigate the noise, the de facto strategy is to apply conventional denoising methods before model training. However, this pre-processing approach may not fully eliminate the effect of noise in SSL for two reasons: (i) the diverse types of noise in time series make it difficult to automatically determine suitable denoising methods; (ii) noise can be amplified after mapping raw data into latent space. In this paper, we propose denoising-aware contrastive learning (DECL), which uses contrastive learning objectives to mitigate the noise in the representation and automatically selects suitable denoising methods for every sample. Extensive experiments on various datasets verify the effectiveness of our method. The code is open-sourced.
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。
