Papers › Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
Zhijie Deng, Yucen Luo, Jun Zhu
Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution. However, such methods typically learn a domain-invariant representation space to match the marginal distributions of the source and target domains, while ignoring their fine-level structures. In this paper, we propose Cluster Alignment with a Teacher (CAT) for unsupervised domain adaptation, which can effectively incorporate the discriminative clustering structures in both domains for better adaptation. Technically, CAT leverages an implicit ensembling teacher model to reliably discover the class-conditional structure in the feature space for the unlabeled target domain. Then CAT forces the features of both the source and the target domains to form discriminative class-conditional clusters and aligns the corresponding clusters across domains. Empirical results demonstrate that CAT achieves state-of-the-art results in several unsupervised domain adaptation scenarios.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | ImageCLEF-DA | rRevGrad+CAT | Accuracy | 80.7 | #14 of 17 | Archive leaderboard | report |
| Domain Adaptation | MNIST-to-USPS | rRevGrad+CAT | Accuracy | 96 | #10 of 14 | Archive leaderboard | report |
| Domain Adaptation | Office-31 | rRevGrad+CAT | Average Accuracy | 80.1 | #34 of 40 | Archive leaderboard | report |
| Domain Adaptation | SVNH-to-MNIST | rRevGrad+CAT | Accuracy | 98.8 | #3 of 9 | Archive leaderboard | report |
| Domain Adaptation | USPS-to-MNIST | MCD+CAT | Accuracy | 96.3 | #11 of 14 | 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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