Papers › In Search of Lost Domain Generalization

In Search of Lost Domain Generalization

2 Jul 2020ICLR 2021 1arXiv:2007.01434archive 2025-07-28

Ishaan Gulrajani, David Lopez-Paz

The goal of domain generalization algorithms is to predict well on distributions different from those seen during training. While a myriad of domain generalization algorithms exist, inconsistencies in experimental conditions -- datasets, architectures, and model selection criteria -- render fair and realistic comparisons difficult. In this paper, we are interested in understanding how useful domain generalization algorithms are in realistic settings. As a first step, we realize that model selection is non-trivial for domain generalization tasks. Contrary to prior work, we argue that domain generalization algorithms without a model selection strategy should be regarded as incomplete. Next, we implement DomainBed, a testbed for domain generalization including seven multi-domain datasets, nine baseline algorithms, and three model selection criteria. We conduct extensive experiments using DomainBed and find that, when carefully implemented, empirical risk minimization shows state-of-the-art performance across all datasets. Looking forward, we hope that the release of DomainBed, along with contributions from fellow researchers, will streamline reproducible and rigorous research in domain generalization.

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Code

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facebookresearch/DomainBed officialmentioned in papermentioned on GitHubpytorch report
YugeTen/fish mentioned on GitHubpytorchMIT report
facebookresearch/domainbed_measures mentioned on GitHubpytorchNOASSERTION report
inouye-lab/feddg_benchmark mentioned on GitHubpytorch report
judydnguyen/pardon-feddg mentioned on GitHubpytorch report
kowshikthopalli/DREAME mentioned on GitHubpytorch report
maryam089/sdvit mentioned on GitHubpytorch report
matsuolab/t3a mentioned on GitHubpytorch report
nancyquris/xdomainmix mentioned on GitHubpytorch report
qiaoruiyt/noiserobustdg mentioned on GitHubpytorchMIT report
weixin-liang/metashift mentioned on GitHubpytorch report

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Code Syntology ran Syntology

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3ran · our draft was wrong
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get_dataset_class facebookresearch/DomainBed/domainbed/datasets.py official repository unverified MIT (permissive) · d0ea85d74c20dea9 · report
num_environments facebookresearch/DomainBed/domainbed/datasets.py official repository unverified MIT (permissive) · 73f32252eedba6a4 · report
accuracy_ent matsuolab/t3a/domainbed/scripts/unsupervised_adaptation.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 5d1f700db67751f1 · report
inv_lr_scheduler kowshikthopalli/DREAME/domainbed/algorithms.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 3ce1df52d4a961b8 · report
softmax_entropy matsuolab/t3a/domainbed/scripts/unsupervised_adaptation.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · e6171e29ed623a12 · report
generate_featurelized_loader matsuolab/t3a/domainbed/scripts/unsupervised_adaptation.py community (archive-listed) unverified MIT (permissive) · 57634636fcd54537 · report
get_algorithm_class identical code first harvested elsewhere unverified licence of this copy not recorded · b0bc80b1655a6802 · report

Tasks

Domain GeneralizationModel Selection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Domain Generalization PACS ERM (Resnet-50, DomainBed) Average Accuracy 85.50 #51 of 133 Archive leaderboard report

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