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Adversarially Adaptive Normalization for Single Domain Generalization
[article]
2021
arXiv
pre-print
Single domain generalization aims to learn a model that performs well on many unseen domains with only one domain data for training. Existing works focus on studying the adversarial domain augmentation (ADA) to improve the model's generalization capability. The impact on domain generalization of the statistics of normalization layers is still underinvestigated. In this paper, we propose a generic normalization approach, adaptive standardization and rescaling normalization (ASR-Norm), to
arXiv:2106.01899v1
fatcat:7o4bilfpnvh6pfrqcw6mbuuxcm