Author proposes a novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks 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 first stage, pseudolabels for the examples in the target domain are estimated using an external unsupervised domain adaptation algorithm—for example, MSTN or CPUA —integrating the proposed domain-specific batch  normalization. The second stage learns the final models using a multi-task classification loss for the source and target domains. Note that the two domains have separate batch normalization layers in both stages.

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.