Self-supervised autoregressive domain adaptation for time series data

Self-supervised autoregressive domain adaptation for time series data

Unsupervised domain adaptation (UDA) has successfully addressed the domain shift problem for visual applications. Yet, these approaches may have limited performance for time series data due to the following reasons. First, they mainly rely on the large-scale dataset (i.e., ImageNet) for source pretr...

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Bibliographic Details
Main Authors: Ragab, Mohamed, Eldele, Emadeldeen, Chen, Zhenghua, Wu, Min, Kwoh, Chee Keong, Li, Xiaoli
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering
Autoregressive Domain Adaptation
Ensemble Teacher Learning
Online Access:https://hdl.handle.net/10356/164530

Internet

https://hdl.handle.net/10356/164530

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