Evidential Multi-Source-Free Unsupervised Domain Adaptation-Reference-Cited by-同舟云学术

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Evidential Multi-Source-Free Unsupervised Domain Adaptation

Published:2024-08 Issue:8 Volume:46 Page:5288-5305
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Pei Jiangbo¹^ORCID,Men Aidong²^ORCID,Liu Yang³^ORCID,Zhuang Xiahai⁴^ORCID,Chen Qingchao¹^ORCID

Affiliation:

1. National Institute of Health Data Science, Peking University, Beijing, China

2. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China

3. Wangxuan Institute of Computer Technology at Peking University, Beijing, China

4. School of Data Science, Fudan University, Shanghai, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/34/10582780/10420513.pdf?arnumber=10420513

Reference86 articles.

1. MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation

2. AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach

3. DLOW: Domain Flow and Applications

4. CDTD: A Large-Scale Cross-Domain Benchmark for Instance-Level Image-to-Image Translation and Domain Adaptive Object Detection

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