Papers › Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization
Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization
Alexandre Rame, Corentin Dancette, Matthieu Cord
Learning robust models that generalize well under changes in the data distribution is critical for real-world applications. To this end, there has been a growing surge of interest to learn simultaneously from multiple training domains - while enforcing different types of invariance across those domains. Yet, all existing approaches fail to show systematic benefits under controlled evaluation protocols. In this paper, we introduce a new regularization - named Fishr - that enforces domain invariance in the space of the gradients of the loss: specifically, the domain-level variances of gradients are matched across training domains. Our approach is based on the close relations between the gradient covariance, the Fisher Information and the Hessian of the loss: in particular, we show that Fishr eventually aligns the domain-level loss landscapes locally around the final weights. Extensive experiments demonstrate the effectiveness of Fishr for out-of-distribution generalization. Notably, Fishr improves the state of the art on the DomainBed benchmark and performs consistently better than Empirical Risk Minimization. Our code is available at https://github.com/alexrame/fishr.
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d6d6cc53be32629d · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Generalization | DomainNet | Fishr (ResNet-50) | Average Accuracy | 41.8 | #38 of 38 | Archive leaderboard | report |
| Domain Generalization | Office-Home | Fishr (ResNet-50) | Average Accuracy | 68.2 | #35 of 45 | Archive leaderboard | report |
| Domain Generalization | PACS | Fishr(ResNet-50,DomainBed) | Average Accuracy | 86.9 | #41 of 133 | Archive leaderboard | report |
| Domain Generalization | TerraIncognita | Fishr(ResNet-50) | Average Accuracy | 47.4 | #30 of 30 | Archive leaderboard | report |
| Domain Generalization | VLCS | Fishr (ResNet-50) | Average Accuracy | 78.2 | #34 of 37 | 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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