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Feature Stylization and Domain-aware Contrastive Learning for Domain Generalization
[article]
2021
arXiv
pre-print
Domain generalization aims to enhance the model robustness against domain shift without accessing the target domain. Since the available source domains for training are limited, recent approaches focus on generating samples of novel domains. Nevertheless, they either struggle with the optimization problem when synthesizing abundant domains or cause the distortion of class semantics. To these ends, we propose a novel domain generalization framework where feature statistics are utilized for
arXiv:2108.08596v1
fatcat:lpnusrrtdvemrbkq55szp6vu6u