Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Kaichao You"'
Autor:
Zexi Jia, Kaichao You, Weihua He, Yang Tian, Yongxiang Feng, Yaoyuan Wang, Xu Jia, Yihang Lou, Jingyi Zhang, Guoqi Li, Ziyang Zhang
Publikováno v:
IEEE Transactions on Image Processing. 32:1829-1842
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b4b2ba58f51b1d6178b85776fb982808
https://doi.org/10.1109/tip.2023.3249579
https://doi.org/10.1109/tip.2023.3249579
Autor:
Yongxiang Feng, Weihua He, Kaichao You, Bing Liu, Ziyang Zhang, Yaoyuan Wang, Minglei Li, Yihang Lou, Jiawei Li, Guoqi Li, Jianxing Liao
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::612ecf243f1268556d61b834364fd8bd
https://doi.org/10.1109/icassp49357.2023.10096430
https://doi.org/10.1109/icassp49357.2023.10096430
RNA virus (e.g., SARS-CoV-2) evolves in a complex manner. Studying RNA virus evolution is vital for understanding molecular evolution and medicine development. Scientists lack, however, general frameworks to characterize the dynamics of RNA virus evo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b1aa7c2fa309583d05f31eedeacd426
http://arxiv.org/abs/2204.08627
http://arxiv.org/abs/2204.08627
Autor:
Song Wu, Kaichao You, Weihua He, Chen Yang, Yang Tian, Yaoyuan Wang, Ziyang Zhang, Jianxing Liao
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200700
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2559cf267bcc76cdd821884764a9d165
https://doi.org/10.1007/978-3-031-20071-7_16
https://doi.org/10.1007/978-3-031-20071-7_16
Autor:
Kaichao You1 YOUKAICHAO@GMAIL.COM, Yong Liu1 LIUYONG21@MAILS.TSINGHUA.EDU.CN, Ziyang Zhang2 ZHANGZIYANG11@HUAWEI.COM, Jianmin Wang1 JIMWANG@TSINGHUA.EDU.CN, Jordan, Michael I.3 JORDAN@CS.BERKELEY.EDU, Mingsheng Long1 MINGSHENG@TSINGHUA.EDU.CN
Publikováno v:
Journal of Machine Learning Research. 2022, Vol. 23, p1-47. 47p.
Publikováno v:
CVPR
Domain adaptation is critical for learning in new and unseen environments. With domain adversarial training, deep networks can learn disentangled and transferable features that effectively diminish the dataset shift between the source and target doma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c23985f398e74fa60332dee1a5a07450
https://doi.org/10.1109/cvpr.2019.00310
https://doi.org/10.1109/cvpr.2019.00310
Publikováno v:
CVPR
Domain Adaptation in Computer Vision with Deep Learning ISBN: 9783030455286
Domain Adaptation in Computer Vision with Deep Learning ISBN: 9783030455286
Domain adaptation aims to transfer knowledge in the presence of the domain gap. Existing domain adaptation methods rely on rich prior knowledge about the relationship between the label sets of source and target domains, which greatly limits their app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ba7f7057ed169632abf9a32b0436b48
https://doi.org/10.1109/cvpr.2019.00283
https://doi.org/10.1109/cvpr.2019.00283
Autor:
Sarafraz, Gita1 (AUTHOR) gita.sarafraz@sharif.edu, Behnamnia, Armin1 (AUTHOR) armin.behnamnia@sharif.edu, Hosseinzadeh, Mehran2 (AUTHOR) mehran.hosseinzadeh1@sharif.edu, Balapour, Ali2 (AUTHOR) ali.balapour@sharif.edu, Meghrazi, Amin2 (AUTHOR) amin.meghrazi@sharif.edu, Rabiee, Hamid R.2 (AUTHOR) rabiee@sharif.edu
Publikováno v:
ACM Computing Surveys. Oct2024, Vol. 56 Issue 10, p1-39. 39p.
Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Aug2024, Vol. 18 Issue 7, p1-53, 53p
Autor:
AYADI, Souha, LACHIRI, Zied
Publikováno v:
Przegląd Elektrotechniczny; 2024, Issue 8, p125-128, 4p