Localized Adversarial Domain Generalization

@article{Zhu2022LocalizedAD,
  title={Localized Adversarial Domain Generalization},
  author={Wei Zhu and Le Lu and Jing Xiao and Mei Han and Jiebo Luo and Adam P. Harrison},
  journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022},
  pages={7098-7108}
}
  • Wei ZhuLe Lu Adam P. Harrison
  • Published 9 May 2022
  • Computer Science
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Deep learning methods can struggle to handle domain shifts not seen in training data, which can cause them to not generalize well to unseen domains. This has led to research attention on domain generalization (DG), which aims to the model's generalization ability to out-of-distribution. Adversarial domain generalization is a popular approach to DG, but conventional approaches (1) struggle to sufficiently align features so that local neighborhoods are mixed across domains; and (2) can suffer… 

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