Papers › Contrastive Vicinal Space for Unsupervised Domain Adaptation
Contrastive Vicinal Space for Unsupervised Domain Adaptation
Jaemin Na, Dongyoon Han, Hyung Jin Chang, Wonjun Hwang
Recent unsupervised domain adaptation methods have utilized vicinal space between the source and target domains. However, the equilibrium collapse of labels, a problem where the source labels are dominant over the target labels in the predictions of vicinal instances, has never been addressed. In this paper, we propose an instance-wise minimax strategy that minimizes the entropy of high uncertainty instances in the vicinal space to tackle the stated problem. We divide the vicinal space into two subspaces through the solution of the minimax problem: contrastive space and consensus space. In the contrastive space, inter-domain discrepancy is mitigated by constraining instances to have contrastive views and labels, and the consensus space reduces the confusion between intra-domain categories. The effectiveness of our method is demonstrated on public benchmarks, including Office-31, Office-Home, and VisDA-C, achieving state-of-the-art performances. We further show that our method outperforms the current state-of-the-art methods on PACS, which indicates that our instance-wise approach works well for multi-source domain adaptation as well. Code is available at https://github.com/NaJaeMin92/CoVi.
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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 | Office-31 | CoVi | Average Accuracy | 91.8 | #6 of 40 | Archive leaderboard | report |
| Domain Adaptation | Office-Home | CoVi | Accuracy | 73.1 | #15 of 29 | Archive leaderboard | report |
| Domain Adaptation | VisDA2017 | CoVi | Accuracy | 88.5 | #10 of 28 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | PACS | CoVi | Average Accuracy | 93.52 | #1 of 3 | 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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