Papers › Domain Generalization via Model-Agnostic Learning of Semantic Features
Domain Generalization via Model-Agnostic Learning of Semantic Features
Qi Dou, Daniel C. Castro, Konstantinos Kamnitsas, Ben Glocker
Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions. We investigate the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can directly generalize to target domains with unknown statistics. We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift. Further, we introduce two complementary losses which explicitly regularize the semantic structure of the feature space. Globally, we align a derived soft confusion matrix to preserve general knowledge about inter-class relationships. Locally, we promote domain-independent class-specific cohesion and separation of sample features with a metric-learning component. The effectiveness of our method is demonstrated with new state-of-the-art results on two common object recognition benchmarks. Our method also shows consistent improvement on a medical image segmentation task.
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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 | PACS | MASF (Resnet-50) | Average Accuracy | 82.67 | #75 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | MASF (Resnet-18) | Average Accuracy | 81.04 | #90 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | MASF (Alexnet) | Average Accuracy | 75.21 | #105 of 133 | 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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