Ishaan Gulrajani, David Lopez-Paz
We lifted 7 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| facebookresearch/DomainBed | canonical | 0 of 2 |
| matsuolab/t3a | extension | 2 of 3 |
| kowshikthopalli/DREAME | extension | 1 of 1 |
| copy not recorded | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| accuracy_ent | Ran | matsuolab/t3a/domainbed/scripts/unsupervised_adaptation.py code served (permissive licence) · get_code("5d1f700db67751f1") |
| inv_lr_scheduler | Ran | kowshikthopalli/DREAME/domainbed/algorithms.py code served (permissive licence) · get_code("3ce1df52d4a961b8") |
| softmax_entropy | Ran | matsuolab/t3a/domainbed/scripts/unsupervised_adaptation.py code served (permissive licence) · get_code("e6171e29ed623a12") |
| generate_featurelized_loader | Not yet run | matsuolab/t3a/domainbed/scripts/unsupervised_adaptation.py code served (permissive licence) · get_code("57634636fcd54537") |
| get_algorithm_class | Not yet run | this paper's copy was not recorded; identical code first harvested from facebookresearch/domainbed pointer only · get_code("b0bc80b1655a6802") |
| get_dataset_class | Not yet run | facebookresearch/DomainBed/domainbed/datasets.py code served (permissive licence) · get_code("d0ea85d74c20dea9") |
| num_environments | Not yet run | facebookresearch/DomainBed/domainbed/datasets.py code served (permissive licence) · get_code("73f32252eedba6a4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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 DO-MAINBED 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. * Alphabetical order, equal contribution. Preprint. Under review.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2007.01434")
get_code_for_paper("2007.01434")
have("2007.01434")
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