Papers › Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, Tatsuya Harada
In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the discriminator. Two problems exist with these methods. First, the domain classifier only tries to distinguish the features as a source or target and thus does not consider task-specific decision boundaries between classes. Therefore, a trained generator can generate ambiguous features near class boundaries. Second, these methods aim to completely match the feature distributions between different domains, which is difficult because of each domain's characteristics. To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries. We propose to maximize the discrepancy between two classifiers' outputs to detect target samples that are far from the support of the source. A feature generator learns to generate target features near the support to minimize the discrepancy. Our method outperforms other methods on several datasets of image classification and semantic segmentation. The codes are available at \url{https://github.com/mil-tokyo/MCD_DA}
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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 | HMDBfull-to-UCF | MCD | Accuracy | 79.34 | #4 of 5 | Archive leaderboard | report |
| Domain Adaptation | MNIST-to-USPS | MCD | Accuracy | 93.8 | #13 of 14 | Archive leaderboard | report |
| Domain Adaptation | SVHN-to-MNIST | MCD | Accuracy | 95.8 | #7 of 14 | Archive leaderboard | report |
| Domain Adaptation | SYNSIG-to-GTSRB | MCD | Accuracy | 94.4 | #3 of 6 | Archive leaderboard | report |
| Domain Adaptation | UCF-to-HMDBfull | MCD | Accuracy | 73.89 | #5 of 5 | Archive leaderboard | report |
| Domain Adaptation | USPS-to-MNIST | MCD | Accuracy | 95.7 | #13 of 14 | Archive leaderboard | report |
| Synthetic-to-Real Translation | Syn2Real-C | MCD | Accuracy | 71.9 | #4 of 6 | 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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