Papers › Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

18 Nov 2019arXiv:1911.07982archive 2025-07-28

Qian Wang, Toby P. Breckon

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.

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hellowangqian/domain-adaptation-capls officialmentioned in papermentioned on GitHub report

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Tasks

Clustering Domain Adaptation Structured Prediction Unsupervised Domain Adaptation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Domain Adaptation	ImageCLEF-DA	SPL	Accuracy	90.3	#4 of 17	Archive leaderboard	report
Domain Adaptation	Office-31	SPL	Average Accuracy	89.6	#18 of 40	Archive leaderboard	report
Domain Adaptation	Office-Caltech	SPL	Average Accuracy	93	#1 of 8	Archive leaderboard	report
Domain Adaptation	Office-Home	SPL	Accuracy	71	#22 of 29	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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