In unsupervised domain adaptation, we have access to a source labeled dataset and a target unlabeled dataset. Then the task is to learn a model that can generalize well to the target dataset. The source and the target datasets vary in terms of distribution. The following figure provides an illustration of this idea. In the present example, we use the MNIST dataset as the source dataset, while the target dataset is SVHN, which consists of images of house numbers. Both datasets have various varying factors in terms of texture, viewpoint, appearence, etc.: their domains, or distributions, are different from one another.