Corpus ID: 232110832

Generalizing to Unseen Domains: A Survey on Domain Generalization

@article{Wang2021GeneralizingTU,
  title={Generalizing to Unseen Domains: A Survey on Domain Generalization},
  author={J. Wang and Cuiling Lan and C. Liu and Yidong Ouyang and Tao Qin},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.03097}
}
Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increased interests in recent years. Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain. For years, great progress has been achieved. This paper presents the first review for recent advances in domain generalization. First, we provide a formal definition of domain… Expand
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SHOWING 1-10 OF 168 REFERENCES
Model-Based Domain Generalization
  • 2
  • PDF
Domain Generalization by Marginal Transfer Learning
  • 17
  • PDF
Feature-Critic Networks for Heterogeneous Domain Generalization
  • 50
  • PDF
Zero-Shot Domain Generalization
  • 2
  • PDF
Domain2Vec: Deep Domain Generalization
  • 3
  • PDF
Batch Normalization Embeddings for Deep Domain Generalization
  • 3
  • PDF
Domain Generalization via Multidomain Discriminant Analysis
  • 16
  • PDF
Domain Generalization via Semi-supervised Meta Learning
  • 1
  • PDF
In Search of Lost Domain Generalization
  • 37
  • PDF
Domain Generalization via Conditional Invariant Representations
  • 30
  • Highly Influential
  • PDF
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