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- W4226205790 abstract "The Visual Domain Adaptation Challenge 2021 called for unsupervised domain adaptation methods that could improve the performance of models by transferring the knowledge obtained from source datasets to out-of-distribution target datasets. In this paper, we utilize BeiT [1] and demonstrate its capability of capturing key attributes from source datasets and apply it to target datasets in a semi-supervised manner. Our method was able to outperform current state-of-the-art (SoTA) techniques and was able to achieve 1st place on the ViSDA Domain Adaptation Challenge with ACC of 56.29% and AUROC of 69.79%." @default.
- W4226205790 created "2022-05-05" @default.
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- W4226205790 date "2021-12-18" @default.
- W4226205790 modified "2023-09-27" @default.
- W4226205790 title "Pre-Training Transformers for Domain Adaptation" @default.
- W4226205790 doi "https://doi.org/10.48550/arxiv.2112.09965" @default.
- W4226205790 hasPublicationYear "2021" @default.
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