Pre-Training Transformers for Domain Adaptation

• URL: http://arxiv.org/abs/2112.09965v1
• Date: Sat, 18 Dec 2021 16:52:48 GMT
• Title: Pre-Training Transformers for Domain Adaptation
• Authors: Burhan Ul Tayyab and Nicholas Chua
• Abstract summary: In this paper, we demonstrate our 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%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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%.

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