Progressively Select and Reject Pseudolabeled Samples for Open-Set Domain Adaptation-Reference-Cited by-同舟云学术

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Progressively Select and Reject Pseudolabeled Samples for Open-Set Domain Adaptation

Published:2024-09 Issue:9 Volume:5 Page:4403-4414
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Wang Qian¹^ORCID,Meng Fanlin²^ORCID,Breckon Toby P.³^ORCID

Affiliation:

1. Department of Computer Science, Durham University, Durham, U.K.

2. Alliance Manchester Business School, The University of Manchester, Manchester, U.K.

3. Department of Computer Science and the Department of Engineering, Durham University, Durham, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9078688/10673734/10478452.pdf?arnumber=10478452

Reference45 articles.

1. Generalized zero-shot domain adaptation via coupled conditional variational autoencoders

2. Neighborhood Preserving and Weighted Subspace Learning Method for Drift Compensation in Gas Sensor

3. VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation

4. Unsupervised Domain Adaptation via Importance Sampling

5. Progressive Feature Alignment for Unsupervised Domain Adaptation

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