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Gradient Matching for Domain Generalization

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arxiv 2104.09937 v3 pith:W3MMOFUD submitted 2021-04-20 cs.LG stat.ML

Yuge Shi , Jeffrey Seely , Philip H.S. Torr , N. Siddharth , Awni Hannun , Nicolas Usunier , Gabriel Synnaeve This is my paper
classification cs.LGstat.ML
keywords benchmarkdatasetsdomaingeneralizationgradientacrosscapturescompetitive
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Machine learning systems typically assume that the distributions of training and test sets match closely. However, a critical requirement of such systems in the real world is their ability to generalize to unseen domains. Here, we propose an inter-domain gradient matching objective that targets domain generalization by maximizing the inner product between gradients from different domains. Since direct optimization of the gradient inner product can be computationally prohibitive -- requires computation of second-order derivatives -- we derive a simpler first-order algorithm named Fish that approximates its optimization. We demonstrate the efficacy of Fish on 6 datasets from the Wilds benchmark, which captures distribution shift across a diverse range of modalities. Our method produces competitive results on these datasets and surpasses all baselines on 4 of them. We perform experiments on both the Wilds benchmark, which captures distribution shift in the real world, as well as datasets in DomainBed benchmark that focuses more on synthetic-to-real transfer. Our method produces competitive results on both benchmarks, demonstrating its effectiveness across a wide range of domain generalization tasks.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization

    cs.LG 2025-06 reject novelty 6.0 of 10

    A unified moment-alignment theory bounds target-domain error by cross-domain differences in loss derivatives, and the new CMA algorithm implements exact gradient and Hessian matching in closed form.

  2. Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization

    cs.CV 2025-07 reject novelty 5.0 of 10

    LEAwareSGD modulates the learning rate with a Lyapunov exponent estimate and claims state-of-the-art accuracy on three single-domain generalization benchmarks, but the method is underspecified and its hyperparameters ...

  3. Technical note on Sequential Test-Time Adaptation via Martingale-Driven Fisher Prompting

    cs.LG 2025-10 conditional novelty 4.0 of 10

    M-FISHER combines an anytime-valid martingale shift detector with Fisher/natural-gradient prompt updates for streaming test-time adaptation of CLIP, with modest empirical gains and largely standard theory.

  4. Single Domain Generalization in Diabetic Retinopathy: A Neuro-Symbolic Learning Approach

    cs.CV 2025-09 reject novelty 4.0 of 10

    KG-DG fuses YOLO-derived lesion features with a frozen ViT via confidence-based fusion and claims gains in diabetic retinopathy domain generalization, but the central KL-divergence mechanism and the MDG headline are c...

  5. Domain-Generalization to Improve Learning in Meta-Learning Algorithms

    cs.LG 2025-08 reject novelty 4.0 of 10

    DGS-MAML layers gradient matching onto SharpMAML and claims O(1/T) convergence and tighter PAC-Bayes bounds, but the displayed theorems give O(1/sqrt T) under the paper's own parameter choices.

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