Zobrazeno 1 - 10
of 580
pro vyhledávání: '"Liu Jiamei"'
Domain adaptation aims to alleviate the domain shift when transferring the knowledge learned from the source domain to the target domain. Due to privacy issues, source-free domain adaptation (SFDA), where source data is unavailable during adaptation,
Externí odkaz:
http://arxiv.org/abs/2310.08928
Publikováno v:
Guoji laonian yixue zazhi, Vol 45, Iss 6, Pp 760-763 (2024)
“Morphological Research Methods” is extensively utilized in scientific research within both basic and clinical medicine. Traditional morphology experiments often lack components such as experimental design, prediction, and analysis of results, wh
Externí odkaz:
https://doaj.org/article/33d76767e2d440d7a5c46b40a4e2bd36
Autor:
LIU Shuai, HE Bin, WANG Tao, LIU Jiamei, CAO Jiawen, WANG Haojie, ZHANG Shuai, LI Kun, LI Ran, ZHANG Yongjun, DOU Xiaodong, WU Zhonghai, CHEN Peng, FENG Chengjun
Publikováno v:
Dizhi lixue xuebao, Vol 30, Iss 2, Pp 314-331 (2024)
Objective On December 18, 2023, an MS 6.2 earthquake occurred in Jishishan County, Gansu Province, China. Coseismic geological hazards induced by the earthquake crucially threatened the safety of personnel and property. Existing research is mainly co
Externí odkaz:
https://doaj.org/article/f3227f95eae34b2798dbc675a7f9d691
Publikováno v:
In Energy 30 December 2024 313
Autor:
Dong, Yudan, Sun, Si, Zheng, Yunzhe, Liu, Jiamei, Zhou, Peng, Xiong, Zhaokun, Zhang, Jing, Pan, Zhi-Cheng, He, Chuan-Shu, Lai, Bo
Publikováno v:
In Journal of Hazardous Materials 5 December 2024 480
Autor:
Chen, Peng, Shu, Siqi, Wu, Zhonghai, Wang, Tao, Feng, Chengjun, Liu, Jiamei, Zhang, Shuai, Wang, Haojie, Li, Kun
Publikováno v:
In Journal of Structural Geology December 2024 189
Publikováno v:
In Journal of Asian Economics December 2024 95
Autor:
Zhu, Decai, Yu, Jie, Zhang, Yingbo, Liu, Jiamei, Ouyang, Yuzhao, Yu, Jiangyu, Liu, Zhongqing, Fan, Wenliang, Bai, Xixi, Wang, Nan, Bu, Erjun, Zhu, Chengjun
Publikováno v:
In International Journal of Hydrogen Energy 26 November 2024 92:1401-1408
Publikováno v:
In Aquaculture Reports October 2024 38
We propose a way of learning disentangled content-style representation of image, allowing us to extrapolate images to any style as well as interpolate between any pair of styles. By augmenting data set in a supervised setting and imposing triplet los
Externí odkaz:
http://arxiv.org/abs/2111.15624