Papers › Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
Feng Liu, Jie Lu, Bo Han, Gang Niu, Guangquan Zhang, Masashi Sugiyama
In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However, in the wild, it is difficult to acquire a large amount of perfectly clean labeled data in SD given limited budget. Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD -- we name it wildly UDA (WUDA). We show that WUDA ruins all UDA methods if taking no care of label noise in SD, and to this end, we propose a Butterfly framework, a powerful and efficient solution to WUDA. Butterfly maintains four deep networks simultaneously, where two take care of all adaptations (i.e., noisy-to-clean, labeled-to-unlabeled, and SD-to-TD-distributional) and then the other two can focus on classification in TD. As a consequence, Butterfly possesses all the conceptually necessary components for solving WUDA. Experiments demonstrate that, under WUDA, Butterfly significantly outperforms existing baseline methods.
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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 | Noisy-Amazon (20%) | Butterfly | Average Accuracy | 71.53 | #1 of 1 | Archive leaderboard | report |
| Domain Adaptation | Noisy-Amazon (45%) | Butterfly | Average Accuracy | 56.01 | #1 of 1 | Archive leaderboard | report |
| Domain Adaptation | Noisy-MNIST-to-SYND | Butterfly | Average Accuracy | 57.55 | #1 of 1 | Archive leaderboard | report |
| Domain Adaptation | Noisy-SYND-to-MNIST | Butterfly | Average Accuracy | 94.09 | #1 of 1 | Archive leaderboard | report |
| Wildly Unsupervised Domain Adaptation | Noisy-Amazon (20%) | Butterfly | Average Accuracy | 71.53 | #1 of 1 | Archive leaderboard | report |
| Wildly Unsupervised Domain Adaptation | Noisy-Amazon (45%) | Butterfly | Average Accuracy | 56.01 | #1 of 1 | Archive leaderboard | report |
| Wildly Unsupervised Domain Adaptation | Noisy-MNIST-to-SYND | Butterfly | Average Accuracy | 57.55 | #1 of 1 | Archive leaderboard | report |
| Wildly Unsupervised Domain Adaptation | Noisy-SYND-to-MNIST | Butterfly | Average Accuracy | 94.09 | #1 of 1 | 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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