Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference

@inproceedings{Liu2021DomainGU,
  title={Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference},
  author={Xiaofeng Liu and Bo Hu and Linghao Jin and Xu Han and Fangxu Xing and Jinsong Ouyang and Jun Lu and Georges El Fakhri and Jonghye Woo},
  booktitle={IJCAI},
  year={2021}
}
  • Xiaofeng Liu, Bo Hu, +6 authors Jonghye Woo
  • Published in IJCAI 22 July 2021
  • Computer Science
In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training. Considering the inherent conditional and label shifts, we would expect the alignment of p(x|y) and p(y). However, the widely used domain invariant feature learning (IFL) methods relies on aligning the marginal concept shift w.r.t. p(x), which rests on an unrealistic assumption that p(y) is invariant across domains… Expand

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