  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}.
