In Search of Lost Domain Generalization

• URL: http://arxiv.org/abs/2007.01434v1
• Date: Thu, 2 Jul 2020 23:08:07 GMT
• Title: In Search of Lost Domain Generalization
• Authors: Ishaan Gulrajani, David Lopez-Paz
• Abstract summary: We implement DomainBed, a testbed for domain generalization. We conduct extensive experiments using DomainBed and find that, when carefully implemented, empirical risk minimization shows state-of-the-art performance.
• Score: 25.43757332883202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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