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Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation
Conference proceeding   Open access

Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation

Shaokai Ye, Kailu Wu, Mu Zhou, Yunfei Yang, Sia Huat Tan, Kaidi Xu, Jiebo Song, Chenglong Bao and Kaisheng Ma
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 13733-13742
Jun 2020
DOI: https://doi.org/10.1109/CVPR42600.2020.01375
url
https://arxiv.org/pdf/1911.12796View
SubmittedarXiv.org - Non-exclusive license to distribute,  Open

Abstract

Adaptation models Data models Feature extraction Gallium nitride Neural networks Performance evaluation Training
Existing domain adaptation methods aim at learning features that can be generalized among domains. These methods commonly require to update source classifier to adapt to the target domain and do not properly handle the trade-off between the source domain and the target domain. In this work, instead of training a classifier to adapt to the target domain, we use a separable component called data calibrator to help the fixed source classifier recover discrimination power in the target domain, while preserving the source domain's performance. When the difference between two domains is small, the source classifier's representation is sufficient to perform well in the target domain and outperforms GAN-based methods in digits. Otherwise, the proposed method can leverage synthetic images generated by GANs to boost performance and achieve state-of-the-art performance in digits datasets and driving scene semantic segmentation. Our method also empirically suggests the potential connection between domain adaptation and adversarial attacks. Code release is available at https://github.com/yeshaokai/ Calibrator-Domain-Adaptation

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26 citations in Scopus

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