• Corpus ID: 237453673

COLUMBUS: Automated Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption

@article{Frikha2021COLUMBUSAD,
  title={COLUMBUS: Automated Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption},
  author={A. Frikha and Denis Krompass and Volker Tresp},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.04320}
}
—Machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts. We address the challenging domain generalization (DG) problem, where a model trained on a set of source domains is expected to generalize well in unseen domains without any exposure to their data. The main challenge of DG is that the features learned from the source domains are not necessarily present in the unseen target domains, leading to… 

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