Contrastive Domain Adaptation for Time-Series Via Temporal Mixup

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Contrastive Domain Adaptation for Time-Series Via Temporal Mixup
Title:
Contrastive Domain Adaptation for Time-Series Via Temporal Mixup
Journal Title:
IEEE Transactions on Artificial Intelligence
Publication Date:
10 July 2023
Citation:
Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C.-K., & Li, X. (2023). Contrastive Domain Adaptation for Time-Series Via Temporal Mixup. IEEE Transactions on Artificial Intelligence, 1–10. https://doi.org/10.1109/tai.2023.3293473
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Programmatic Funds
Grant Reference no. : A20H6b0151

This research / project is supported by the A*STAR - Career Development Award
Grant Reference no. : C210112046
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2691-4581
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