Style Normalization and Restitution for DomainGeneralization and Adaptation

@article{Jin2021StyleNA,
  title={Style Normalization and Restitution for DomainGeneralization and Adaptation},
  author={Xin Jin and Cuiling Lan and Wenjun Zeng and Zhibo Chen},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.00588}
}
For many practical computer vision applications, the learned models usually have high performance on the datasets used for training but suffer from significant performance degradation when deployed in new environments, where there are usually style differences between the training images and the testing images. An effective domain generalizable model is expected to be able to learn feature representations that are both generalizable and discriminative. In this paper, we design a novel Style… 
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