Self-supervised autoregressive domain adaptation for time series data

• المؤلفون الرئيسيون: Ragab, Mohamed, Eldele, Emadeldeen, Chen, Zhenghua, Wu, Min, Kwoh, Chee Keong, Li, Xiaoli
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Computer science and engineering; Autoregressive Domain Adaptation; Ensemble Teacher Learning
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/164530
• المؤسسة: Nanyang Technological University

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...