Papers › Confidence Regularized Self-Training
Confidence Regularized Self-Training
Yang Zou, Zhiding Yu, Xiaofeng Liu, B. V. K. Vijaya Kumar, Jinsong Wang
Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation. These methods often involve an iterative process of predicting on target domain and then taking the confident predictions as pseudo-labels for retraining. However, since pseudo-labels can be noisy, self-training can put overconfident label belief on wrong classes, leading to deviated solutions with propagated errors. To address the problem, we propose a confidence regularized self-training (CRST) framework, formulated as regularized self-training. Our method treats pseudo-labels as continuous latent variables jointly optimized via alternating optimization. We propose two types of confidence regularization: label regularization (LR) and model regularization (MR). CRST-LR generates soft pseudo-labels while CRST-MR encourages the smoothness on network output. Extensive experiments on image classification and semantic segmentation show that CRSTs outperform their non-regularized counterpart with state-of-the-art performance. The code and models of this work are available at https://github.com/yzou2/CRST.
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90009a990acf6c4d · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | Office-31 | MRKLD + LRENT | Average Accuracy | 86.8 | #26 of 40 | Archive leaderboard | report |
| Domain Adaptation | VisDA2017 | MRKLD + LRENT | Accuracy | 78.1 | #21 of 28 | Archive leaderboard | report |
| Domain Adaptation | VisDA2017 | CRST | Accuracy | 78.1 | #22 of 28 | Archive leaderboard | report |
| Image-to-Image Translation | SYNTHIA-to-Cityscapes | LRENT (DeepLabv2) | mIoU (13 classes) | 48.7 | #18 of 28 | Archive leaderboard | report |
| Semantic Segmentation | DensePASS | CRST | mIoU | 31.67% | #24 of 36 | Archive leaderboard | report |
| Synthetic-to-Real Translation | GTAV-to-Cityscapes Labels | CRST(MRKLD-SP-MST) | mIoU | 49.8 | #50 of 73 | 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.
Methods
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