Deep Into the Domain Shift: Transfer Learning Through Dependence Regularization-Reference-Cited by-同舟云学术

Deep Into the Domain Shift: Transfer Learning Through Dependence Regularization

Published:2023 Issue: Volume: Page:1-15
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Ma Shumin¹^ORCID,Yuan Zhiri²^ORCID,Wu Qi³^ORCID,Huang Yiyan²,Hu Xixu²,Leung Cheuk Hang²,Wang Dongdong⁴,Huang Zhixiang⁴

Affiliation:

1. Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science and the Division of Science and Technology, BNU-HKBU United International College, Zhuhai, China

2. CityU-JD Digits Joint Laboratory in Financial Technology and Engineering and the School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong

3. School of Data Science, the CityU-JD Digits Joint Laboratory in Financial Technology and Engineering, and the Institute of Data Science, City University of Hong Kong, Hong Kong, Hong Kong

4. JD Digits Technology, Beijing, China

Funder

InnoHK initiative

Government of the Hong Kong Special Administrative Region

Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science

BNU-HKBU United International College

Guangdong Higher Education Upgrading Plan

Hong Kong Research Grants Council through the General Research Fund

CityU SRG-Fd Fund

CityU-JD Digits Laboratory in Financial Technology and Engineering, HK Institute of Data Science

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10144691.pdf?arnumber=10144691