CrossNorm and SelfNorm for Generalization under Distribution Shifts

2021 IEEE/CVF International Conference on Computer Vision (ICCV)(2021)

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摘要
Traditional normalization techniques (e.g., Batch Normalization and Instance Normalization) generally and simplistically assume that training and test data follow the same distribution. As distribution shifts are inevitable in real-world applications, well-trained models with previous normalization methods can perform badly in new environments. Can we develop new normalization methods to improve g...
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Training,Bridges,Computer vision,Codes,Robustness,Task analysis
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