Unsupervised Domain Adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabeled target domain. Despite the prevalence of UDA for visual applications, it remains relatively less explored for time-series applications. In this work, we propose a novel lightweight contrastive domain adaptation framework called CoTMix for time-series data. Unlike existing approaches that either use statistical distances or adversarial techniques, we leverage contrastive learning solely to mitigate the distribution shift across the different domains. Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains. Subsequently, we leverage contrastive learning to maximize the similarity between each domain and its corresponding augmented view. The generated views consider the temporal dynamics of time-series data during the adaptation process while inheriting the semantics among the two domains. Hence, we gradually push both domains towards a common intermediate space, mitigating the distribution shift across them. Extensive experiments conducted on five real-world time-series datasets show that our approach can significantly outperform all state-of-the-art UDA methods. The implementation code of CoTMix is available at \href{https://github.com/emadeldeen24/CoTMix}{github.com/emadeldeen24/CoTMix}.

翻译：无监督域适应（UDA）通过将标记源域的知识迁移至分布偏移的无标记目标域，已成为解决领域偏移问题的强大方案。尽管UDA在视觉应用中广泛流行，但其在时间序列领域的探索仍相对不足。本文针对时间序列数据，提出一种名为CoTMix的新型轻量级对比域适应框架。与现有采用统计距离或对抗技术的方案不同，我们仅利用对比学习来缓解不同域之间的分布偏移。具体而言，我们提出一种新颖的时间混合增强策略，为源域和目标域生成两个中间增强视图。随后，我们利用对比学习最大化每个域与其对应增强视图之间的相似性。所生成的视图在适应过程中兼顾时间序列的时间动态特性，同时继承两个域之间的语义信息。因此，我们逐步将两个域推向共同的中间空间，从而缓解跨域分布偏移。在五个真实时间序列数据集上开展的大量实验表明，我们的方法显著优于所有最先进的UDA方法。CoTMix的实现代码见\href{https://github.com/emadeldeen24/CoTMix}{github.com/emadeldeen24/CoTMix}。