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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 11 Pith papers

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  2. Continual Learning of Domain-Invariant Representations

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    Evaluates four distribution shifts in sensor-based HAR, finds diversity shifts dominate, and shows 28 DG methods only marginally beat ERM while releasing open benchmarks.

  4. Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization

    cs.LG 2022-09 unverdicted novelty 6.0 of 10

    FGMix learns instance weights via gradient compatibilities to perform mixup with extrapolation toward flatter minima, outperforming prior DG methods on DomainBed.

  5. Towards Truly Multilingual ASR: Generalizing Code-Switching ASR to Unseen Language Pairs

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