Papers › Open Set Domain Adaptation: Theoretical Bound and Algorithm
Open Set Domain Adaptation: Theoretical Bound and Algorithm
Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain -- the basic strategy being to mitigate the effects of discrepancies between the two distributions. Most existing algorithms can only handle unsupervised closed set domain adaptation (UCSDA), i.e., where the source and target domains are assumed to share the same label set. In this paper, we target a more challenging but realistic setting: unsupervised open set domain adaptation (UOSDA), where the target domain has unknown classes that are not found in the source domain. This is the first study to provide a learning bound for open set domain adaptation, which we do by theoretically investigating the risk of the target classifier on unknown classes. The proposed learning bound has a special term, namely open set difference, which reflects the risk of the target classifier on unknown classes. Further, we present a novel and theoretically guided unsupervised algorithm for open set domain adaptation, called distribution alignment with ppen difference (DAOD), which is based on regularizing this open set difference bound. The experiments on several benchmark datasets show the superior performance of the proposed UOSDA method compared with the state-of-the-art methods in the literature.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | Office-31 | DAOD | Average Accuracy | 85.4 | #29 of 40 | Archive leaderboard | report |
| Domain Adaptation | Office-Home | DAOD | Accuracy | 69.8 | #24 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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