Papers › Learning to Balance Specificity and Invariance for In and Out of Domain Generalization
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization
Prithvijit Chattopadhyay, Yogesh Balaji, Judy Hoffman
We introduce Domain-specific Masks for Generalization, a model for improving both in-domain and out-of-domain generalization performance. For domain generalization, the goal is to learn from a set of source domains to produce a single model that will best generalize to an unseen target domain. As such, many prior approaches focus on learning representations which persist across all source domains with the assumption that these domain agnostic representations will generalize well. However, often individual domains contain characteristics which are unique and when leveraged can significantly aid in-domain recognition performance. To produce a model which best generalizes to both seen and unseen domains, we propose learning domain specific masks. The masks are encouraged to learn a balance of domain-invariant and domain-specific features, thus enabling a model which can benefit from the predictive power of specialized features while retaining the universal applicability of domain-invariant features. We demonstrate competitive performance compared to naive baselines and state-of-the-art methods on both PACS and DomainNet.
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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 | DomainNet | DMG (ResNet-50) | Average Accuracy | 43.63 | #35 of 38 | Archive leaderboard | report |
| Domain Generalization | DomainNet | MetaReg (ResNet-50) | Average Accuracy | 43.62 | #36 of 38 | Archive leaderboard | report |
| Domain Generalization | PACS | DMG (Resnet-50) | Average Accuracy | 83.37 | #69 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | DMG (Resnet-18) | Average Accuracy | 81.46 | #87 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | DMG (Alexnet) | Average Accuracy | 73.32 | #111 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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