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公开(公告)号：US20210295208A1
公开(公告)日：2021-09-23
申请号：US16826084
申请日：2020-03-20
发明人： Pengfei Xu , Sicheng Zhao , Guangzhi Wang , Shanghang Zhang , Yang Gu , Yaxian Li , Zhichao Song , Runbo Hu , Hua Chai
摘要： Embodiments of the disclosure provide systems and methods for domain adaptation between a plurality of source domains and a target domain. The artificial intelligence method includes receiving labeled data from the plurality of source domains and unlabeled data from the target domain. The method further includes separately training, by a processor, a plurality of source classifiers each corresponding to a source domain using the labeled data received from the respective source domains. The method also includes selecting a subset of the labeled data received from each source domain based on a similarity between the selected labeled data and the unlabeled data of the target domain. The method additionally includes refining, by the processor, each source classifier using the selected subset of the labeled data, and predicting labels of the unlabeled data using the refined source classifiers.
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公开(公告)号：US11526807B2
公开(公告)日：2022-12-13
申请号：US16826084
申请日：2020-03-20
发明人： Pengfei Xu , Sicheng Zhao , Guangzhi Wang , Shanghang Zhang , Yang Gu , Yaxian Li , Zhichao Song , Runbo Hu , Hua Chai
摘要： Embodiments of the disclosure provide systems and methods for domain adaptation between a plurality of source domains and a target domain. The artificial intelligence method includes receiving labeled data from the plurality of source domains and unlabeled data from the target domain. The method further includes separately training, by a processor, a plurality of source classifiers each corresponding to a source domain using the labeled data received from the respective source domains. The method also includes selecting a subset of the labeled data received from each source domain based on a similarity between the selected labeled data and the unlabeled data of the target domain. The method additionally includes refining, by the processor, each source classifier using the selected subset of the labeled data, and predicting labels of the unlabeled data using the refined source classifiers.
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