Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Ding, Xuejie"'
Autor:
Ding, Xuejie, Liu, Yang, Wan, Shuhui, Yang, Yueru, Liang, Ruyi, Yang, Shijie, Zhang, Jiake, Cao, Xiuyu, Zhou, Min, Chen, Weihong
Publikováno v:
In Environmental Pollution 1 November 2024 360
Autor:
Wang, Shusen, Du, Yuanyuan, Zhang, Boya, Meng, Gaofan, Liu, Zewen, Liew, Soon Yi, Liang, Rui, Zhang, Zhengyuan, Cai, Xiangheng, Wu, Shuangshuang, Gao, Wei, Zhuang, Dewei, Zou, Jiaqi, Huang, Hui, Wang, Mingyang, Wang, Xiaofeng, Wang, Xuelian, Liang, Ting, Liu, Tengli, Gu, Jiabin, Liu, Na, Wei, Yanling, Ding, Xuejie, Pu, Yue, Zhan, Yixiang, Luo, Yu, Sun, Peng, Xie, Shuangshuang, Yang, Jiuxia, Weng, Yiqi, Zhou, Chunlei, Wang, Zhenglu, Wang, Shuang, Deng, Hongkui, Shen, Zhongyang
Publikováno v:
In Cell 31 October 2024 187(22):6152-6164
Autor:
Zhang, Jiake, Hu, Yuxiang, Wang, Xing, Ding, Xuejie, Cen, Xingzu, Wang, Bin, Yang, Shijie, Ye, Zi, Qiu, Weihong, Chen, Weihong, Zhou, Min
Publikováno v:
In Chemosphere September 2024 364
Autor:
Zhang, Jiake, Ding, Xuejie, Tan, Qiyou, Liang, Ruyi, Chen, Bingdong, Yu, Linling, Wang, Mengyi, Qing, Mengxia, Yang, Shijie, Li, Yonggang, Chen, Weihong, Zhou, Min
Publikováno v:
In Environmental Research 1 December 2024 262 Part 2
Semantic segmentation based on deep learning methods can attain appealing accuracy provided large amounts of annotated samples. However, it remains a challenging task when only limited labelled data are available, which is especially common in medica
Externí odkaz:
http://arxiv.org/abs/2110.11998
We propose a cross-domain latent modulation mechanism within a variational autoencoders (VAE) framework to enable improved transfer learning. Our key idea is to procure deep representations from one data domain and use it as perturbation to the repar
Externí odkaz:
http://arxiv.org/abs/2012.11727
Autor:
Liu, Yang, Ding, Xuejie, Yu, Linling, Shi, Da, Liang, Ruyi, Liu, Wei, Huang, Xuezan, Cao, Xiuyu, Zhou, Min, Chen, Weihong
Publikováno v:
In Journal of Environmental Sciences April 2025 150:412-421
Publikováno v:
Neural Networks, Volume 149, 2022, Pages 172-183
Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains. In this paper, we present a novel "deep adversarial transition learning" (DATL) fram
Externí odkaz:
http://arxiv.org/abs/2009.12028
Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains. In this paper, we present a novel approach that bridges the domain gap by projecting
Externí odkaz:
http://arxiv.org/abs/1902.06328
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