Papers › Reducing Domain Gap by Reducing Style Bias
Reducing Domain Gap by Reducing Style Bias
Hyeonseob Nam, Hyunjae Lee, Jongchan Park, Wonjun Yoon, Donggeun Yoo
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs' strong inductive bias towards image styles (i.e. textures) which are sensitive to domain changes, rather than contents (i.e. shapes). Inspired by this, we propose to reduce the intrinsic style bias of CNNs to close the gap between domains. Our Style-Agnostic Networks (SagNets) disentangle style encodings from class categories to prevent style biased predictions and focus more on the contents. Extensive experiments show that our method effectively reduces the style bias and makes the model more robust under domain shift. It achieves remarkable performance improvements in a wide range of cross-domain tasks including domain generalization, unsupervised domain adaptation, and semi-supervised domain adaptation on multiple datasets.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Generalization | Office-Home | SagNet (ResNet-18) | Average Accuracy | 62.34 | #44 of 45 | Archive leaderboard | report |
| Domain Generalization | PACS | SagNet (Resnet-18) | Average Accuracy | 83.25 | #71 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | SagNet (Resnet-50) | Average Accuracy | 82.3 | #79 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | SagNet (Alexnet) | Average Accuracy | 75.52 | #104 of 133 | Archive leaderboard | report |
| Image to sketch recognition | PACS | SagNet (ResNet18) | Accuracy | 40.7 | #6 of 7 | Archive leaderboard | report |
| Single-Source Domain Generalization | PACS | SagNet (ResNet18) | Accuracy | 61.9 | #9 of 10 | 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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