Pre-Training Transformers for Domain Adaptation

Burhan Ul Tayyab; Nicholas Chua

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arXiv · 2112.09965

Pre-Training Transformers for Domain Adaptation

Burhan Ul Tayyab·Nicholas Chua

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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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Burhan Ul Tayyab, Nicholas Chua. 2021-12-18. Pre-Training Transformers for Domain Adaptation. https://arxiv.org/abs/2112.09965

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