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pro vyhledávání: '"Ren, Chuan-Xian"'
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
Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textu
Externí odkaz:
http://arxiv.org/abs/2303.02943
Unsupervised domain adaptation (UDA) is one of the prominent tasks of transfer learning, and it provides an effective approach to mitigate the distribution shift between the labeled source domain and the unlabeled target domain. Prior works mainly fo
Externí odkaz:
http://arxiv.org/abs/2203.06836
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
Autor:
Xu, Geng-Xin, Ren, Chuan-Xian
Segmentation of retinal vessel images is critical to the diagnosis of retinopathy. Recently, convolutional neural networks have shown significant ability to extract the blood vessel structure. However, it remains challenging to refined segmentation f
Externí odkaz:
http://arxiv.org/abs/2202.09515
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:
Xu, Geng-Xin, Liu, Chen, Liu, Jun, Ding, Zhongxiang, Shi, Feng, Guo, Man, Zhao, Wei, Li, Xiaoming, Wei, Ying, Gao, Yaozong, Ren, Chuan-Xian, Shen, Dinggang
Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the
Externí odkaz:
http://arxiv.org/abs/2109.03478
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
Publikováno v:
In Medical Image Analysis April 2024 93