Papers › Instance Adaptive Self-Training for Unsupervised Domain Adaptation
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
Ke Mei, Chuang Zhu, Jiaqi Zou, Shanghang Zhang
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such a problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing scalability and performance. In this paper, we propose an instance adaptive self-training framework for UDA on the task of semantic segmentation. To effectively improve the quality of pseudo-labels, we develop a novel pseudo-label generation strategy with an instance adaptive selector. Besides, we propose the region-guided regularization to smooth the pseudo-label region and sharpen the non-pseudo-label region. Our method is so concise and efficient that it is easy to be generalized to other unsupervised domain adaptation methods. Experiments on 'GTA5 to Cityscapes' and 'SYNTHIA to Cityscapes' demonstrate the superior performance of our approach compared with the state-of-the-art 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 | SYNTHIA-to-Cityscapes | IAST (ResNet-101) | mIoU | 49.8 | #20 of 33 | Archive leaderboard | report |
| Image-to-Image Translation | SYNTHIA-to-Cityscapes | IAST(ResNet-101) | mIoU (13 classes) | 57.0 | #13 of 28 | Archive leaderboard | report |
| Synthetic-to-Real Translation | GTAV-to-Cityscapes Labels | IAST | mIoU | 51.5 | #42 of 73 | Archive leaderboard | report |
| Synthetic-to-Real Translation | SYNTHIA-to-Cityscapes | IAST(ResNet-101) | MIoU (13 classes) | 57.0 | #23 of 38 | Archive leaderboard | report |
| Synthetic-to-Real Translation | SYNTHIA-to-Cityscapes | IAST(ResNet-101) | MIoU (16 classes) | 49.8 | #23 of 38 | 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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