Abstract We propose a novel unsupervised domain adaptation framework based on domain-specifific batch normalization in deep neural networks. We aim to adapt to both domains by specializing batch normalization layers in convolutional neural networks while allowing them to share all other model parameters, which is realized by a twostage algorithm. In the fifirst stage, we estimate pseudolabels for the examples in the target domain using an external unsupervised domain adaptation algorithm—for example, MSTN [27] or CPUA [14]—integrating the proposed domain-specifific batch normalization. The second stage learns the fifinal models using a multi-task classifification loss for the source and target domains. Note that the two domains have separate batch normalization layers in both stages. Our framework can be easily incorporated into the domain adaptation techniques based on deep neural networks with batch normalization layers. We also present that our approach can be extended to the problem with multiple source domains. The proposed algorithm is evaluated on multiple benchmark datasets and achieves the state-of-theart accuracy in the standard setting and the multi-source domain adaption scenario