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pro vyhledávání: '"Luo, You-Wei"'
To overcome the restriction of identical distribution assumption, invariant representation learning for unsupervised domain adaptation (UDA) has made significant advances in computer vision and pattern recognition communities. In UDA scenario, the tr
Externí odkaz:
http://arxiv.org/abs/2407.09524
Autor:
Luo, You-Wei, Ren, Chuan-Xian
As a crucial step toward real-world learning scenarios with changing environments, dataset shift theory and invariant representation learning algorithm have been extensively studied to relax the identical distribution assumption in classical learning
Externí odkaz:
http://arxiv.org/abs/2406.16608
Autor:
Zhai, Yi-Ming, Luo, You-Wei
Unsupervised domain adaptation studies how to transfer a learner from a labeled source domain to an unlabeled target domain with different distributions. Existing methods mainly focus on matching the marginal distributions of the source and target do
Externí odkaz:
http://arxiv.org/abs/2203.03212
Autor:
Luo, You-Wei, Ren, Chuan-Xian
As a fundamental problem in machine learning, dataset shift induces a paradigm to learn and transfer knowledge under changing environment. Previous methods assume the changes are induced by covariate, which is less practical for complex real-world da
Externí odkaz:
http://arxiv.org/abs/2202.13043
Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain in the presence of dataset shift. Most existing methods cannot address the domain alignment and class discrimination wel
Externí odkaz:
http://arxiv.org/abs/2112.11041
Autor:
Luo, You-Wei, Ren, Chuan-Xian
As a vital problem in classification-oriented transfer, unsupervised domain adaptation (UDA) has attracted widespread attention in recent years. Previous UDA methods assume the marginal distributions of different domains are shifted while ignoring th
Externí odkaz:
http://arxiv.org/abs/2108.00302
Unsupervised domain adaptation is effective in leveraging rich information from a labeled source domain to an unlabeled target domain. Though deep learning and adversarial strategy made a significant breakthrough in the adaptability of features, ther
Externí odkaz:
http://arxiv.org/abs/2008.10030
Publikováno v:
IEEE Transactions on Image Processing, vol. 29, pp. 2875-2888, 2020
Image set recognition has been widely applied in many practical problems like real-time video retrieval and image caption tasks. Due to its superior performance, it has grown into a significant topic in recent years. However, images with complicated
Externí odkaz:
http://arxiv.org/abs/2008.09994
Unsupervised domain adaptation is effective in leveraging the rich information from the source domain to the unsupervised target domain. Though deep learning and adversarial strategy make an important breakthrough in the adaptability of features, the
Externí odkaz:
http://arxiv.org/abs/2002.08675
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