Unsupervised domain adaptation in the wild via disentangling representation learning

Most recently proposed unsupervised domain adaptation algorithms attempt to learn domain invariant features by confusing a domain classifier through adversarial training. In this paper, we argue that this may not be an optimal solution in the real-world setting (a.k.a. in the wild) as the difference...

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Bibliographic Details
Main Authors: Li, Haoliang, Wan, Renjie, Wang, Shiqi, Kot, Alex Chichung
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160950
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Institution: Nanyang Technological University
Language: English