Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization

This is an official implementation of “Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization” (ACMMM 2021 Oral)

Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization

Seogkyu Jeon, Kibeom Hong, Pilhyeon Lee, Jewook Lee, Hyeran Byun (Yonsei Univ.)

Paper : https://arxiv.org/abs/2108.08596

Abstract: 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 stylizing original features to ones with novel domain properties. To preserve class information during stylization, we first decompose

 

 

 

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