Jul 20, 2020 · We propose an unsupervised method for adapting a source classifier to a target domain that varies from the source domain along natural axes, ...
In this paper, we investigate a challenging unsupervised domain adaptation setting — unsupervised model adapta- tion. We aim to explore how to rely only on ...
Abstract: In this paper, we investigate a challenging unsupervised domain adaptation setting --- unsupervised model adaptation.
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In this paper we address the domain adaptation problem in real world applications, where the reuse of source domain data is lim- ited to classification rules or ...
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Jul 20, 2020 · Abstract. Current unsupervised domain adaptation methods can address many types of distribution shift, but they assume data from the source ...
Dec 14, 2020 · This paper aims to tackle a realistic setting with only a classification model available trained over, instead of accessing to, the source data.
This paper aims at answering the question of finding the right strategy to make the target model robust and accurate in the setting of unsupervised domain ...
Jan 15, 2024 · This work considers techniques that use only a trained source model instead of a huge amount of source data to make domain adaption.
In this paper we address the domain adaptation problem in real world applications, where the reuse of source domain data is limited to classification rules or a ...
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled target domain, but it requires to access the source data which often ...