Domain-Conditioned Normalization for Test-Time Domain Generalization.

ECCV Workshops (8)(2022)

引用 0|浏览10
暂无评分
摘要
Domain generalization aims to train a robust model on multiple source domains that generalizes well to unseen target domains. While considerable attention has focused on training domain generalizable models, a few recent studies have shifted the attention to test time, i.e., leveraging test samples for better target generalization. To this end, this paper proposes a novel test-time domain generalization method, Domain Conditioned Normalization (DCN), to infer the normalization statistics of the target domain from only a single test sample. In order to learn to predict the normalization statistics, DCN adopts a meta-learning framework and simulates the inference process of the normalization statistics at training. Extensive experimental results have shown that DCN brings consistent improvements to many state-of-the-art domain generalization methods on three widely adopted benchmarks.
更多
查看译文
关键词
normalization,domain-conditioned,test-time
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要