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Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

Wang, Q.; Breckon, T.P.

Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling Thumbnail


Q. Wang


Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain and the data distributions are different in these two domains. As a result, classifiers trained from labeled samples in the source domain suffer from significant performance drop when directly applied to the samples from the target domain. To address this issue, different approaches have been proposed to learn domain-invariant features or domain-specific classifiers. In either case, the lack of labeled samples in the target domain can be an issue which is usually overcome by pseudo-labeling. Inaccurate pseudo-labeling, however, could result in catastrophic error accumulation during learning. In this paper, we propose a novel selective pseudo-labeling strategy based on structured prediction. The idea of structured prediction is inspired by the fact that samples in the target domain are well clustered within the deep feature space so that unsupervised clustering analysis can be used to facilitate accurate pseudo-labeling. Experimental results on four datasets (i.e. Office-Caltech, Office31, ImageCLEF-DA and Office-Home) validate our approach outperforms contemporary state-of-the-art methods.


Wang, Q., & Breckon, T. (2020). Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling. In AAAI-20 / IAAI-20 / EAAI-20 proceedings (6243-6250).

Conference Name Thirty Fourth AAAI Conference on Artificial Intelligence
Conference Location New York, USA
Start Date Feb 7, 2020
End Date Feb 12, 2020
Acceptance Date Nov 11, 2019
Online Publication Date Apr 3, 2020
Publication Date 2020-04
Deposit Date Dec 20, 2019
Publicly Available Date Mar 18, 2020
Volume 34
Pages 6243-6250
Series Number 4
Series ISSN 2159-5399,2374-3468
Book Title AAAI-20 / IAAI-20 / EAAI-20 proceedings


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