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of 639
pro vyhledávání: '"Kwoh Chee Keong"'
This work addresses the challenging domain adaptation setting in which knowledge from the labelled source domain dataset is available only from the pretrained black-box segmentation model. The pretrained model's predictions for the target domain imag
Externí odkaz:
http://arxiv.org/abs/2307.00893
Obtaining sufficient labeled data for training deep models is often challenging in real-life applications. To address this issue, we propose a novel solution for single-source domain generalized semantic segmentation. Recent approaches have explored
Externí odkaz:
http://arxiv.org/abs/2304.09347
Label-efficient time series representation learning, which aims to learn effective representations with limited labeled data, is crucial for deploying deep learning models in real-world applications. To address the scarcity of labeled time series dat
Externí odkaz:
http://arxiv.org/abs/2302.06433
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 applicatio
Externí odkaz:
http://arxiv.org/abs/2212.01555
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 possessing a massive amount of labeled data for training, limiting their app
Externí odkaz:
http://arxiv.org/abs/2210.06286
Autor:
Eldele, Emadeldeen, Ragab, Mohamed, Chen, Zhenghua, Wu, Min, Kwoh, Chee-Keong, Li, Xiaoli, Guan, Cuntai
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 shown great improvement in extracting useful representations from unlabel
Externí odkaz:
http://arxiv.org/abs/2208.06616
Autor:
Ragab, Mohamed, Eldele, Emadeldeen, Tan, Wee Ling, Foo, Chuan-Sheng, Chen, Zhenghua, Wu, Min, Kwoh, Chee-Keong, Li, Xiaoli
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 typically developed on image data, and their application to time series dat
Externí odkaz:
http://arxiv.org/abs/2203.08321
Autor:
Li, Chenyang, Mo, Lingfei, Kwoh, Chee Keong, Li, Xiaoli, Chen, Zhenghua, Wu, Min, Yan, Ruqiang
Publikováno v:
In Mechanical Systems and Signal Processing 1 January 2025 224
Autor:
Lin, Zhuoyi, Zang, Sheng, Wang, Rundong, Sun, Zhu, Senthilnath, J., Xu, Chi, Kwoh, Chee-Keong
Re-ranking models refine item recommendation lists generated by the prior global ranking model, which have demonstrated their effectiveness in improving the recommendation quality. However, most existing re-ranking solutions only learn from implicit
Externí odkaz:
http://arxiv.org/abs/2201.05333
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 large-s
Externí odkaz:
http://arxiv.org/abs/2111.14834