216 citations · 540 across the 21 of their papers we have counts for
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Contrastive Domain Adaptation for Time-Series via Temporal Mixup
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +3
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 unlabe…
Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +3
The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC). However, the success of these models is attributed to posses…
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +4
Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has sho…
Domain Generalization via Selective Consistency Regularization for Time Series Classification
Wenyu Zhang, Mohamed Ragab, Chuan-Sheng Foo
Domain generalization methods aim to learn models robust to domain shift with data from a limited number of source domains and without access to target domain samples during traini…
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
Mohamed Ragab, Emadeldeen Eldele, Wee Ling Tan +5
Unsupervised domain adaptation methods aim to generalize well on unlabeled test data that may have a different (shifted) distribution from the training data. Such methods are typic…