Evaluation of Generative Unsupervised Domain Adaptation in the Absence of Target Labels

Unsupervised domain adaptation is essential for generalization on unlabeled target domains. Generative domain adaptation methods achieve domain adaptation by synthesizing intermediate source-to-target images. The inspection of such images can assist in identifying successful sets of hyperparameters and methods, however, this is both time-consuming and frequently challenging. In practical applications, selecting an appropriate method and tuning its parameters is difficult when target labels are entirely absent. We develop a metric for automatically assessing unsupervised generative domain adaptation methods based on the generated source-to-target images. We show that this metric correlates well with the performance of the downstream machine learning task, which is, in this case, semantic segmentation.