Trained deep learning models do not generalize well if the testing data has a different distribution from the training data set. For instance, in medical image segmentation, the MRI and CT scan of the same object look very different. If we simply train a model on the MRI scans, it is very likely that the model will not work on the CT scans. However, it is very expensive and time-consuming to manually label different data sets. Therefore, we wish to transfer the knowledge from a labeled training set to an unlabeled testing data with a different distribution. Domain adaptation can help us achieve this purpose.

Domain adaptation can be categorized into three types based on the availability of target domain data: supervised, semi-supervised, unsupervised [1]. In supervised domain adaptation, a limited amount of labeled target domain data is available. In the semi-supervised setting, unlabeled target domain data as well as a small amount of labeled target domain data is available. In the unsupervised setting, only unlabeled target domain data is available. Unsupervised domain adaptation is an ill-posed problem since we do not have labels for the target domain data. Proper assumptions on the target domain data are important for performing unsupervised domain adaptation. In our research, we focus on the unsupervised domain adaptation. Unsupervised domain adaptation can be applied to many computer vision problems, including classification, segmentation, and detection. Currently, we focus our experiment on classification.

–By Ruiyuan Lin



[1] M. Wang and W. Deng, “Deep visual domain adaptation: A survey,” Neurocomputing, 2018.

Image Credits:

Anon, (2018). Available at: [Accessed 16 Dec. 2018].

X. Peng,  B. Usman,  N. Kaushik,  J. Hoffman, D.  Wang, and K. Saenko, “Visda:  The visual domain adaptation challenge,” 2017.