{
  "id": "2104.09937",
  "version": "v1",
  "published": "2021-04-20T12:55:37.000Z",
  "updated": "2021-04-20T12:55:37.000Z",
  "title": "Gradient Matching for Domain Generalization",
  "authors": [
    "Yuge Shi",
    "Jeffrey Seely",
    "Philip H. S. Torr",
    "N. Siddharth",
    "Awni Hannun",
    "Nicolas Usunier",
    "Gabriel Synnaeve"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-04-20T12:55:37.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "domain generalization",
      "gradient matching",
      "method produces competitive results",
      "captures distribution shift",
      "learning systems typically assume"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}