Adversarial Domain Adaptation Enhanced via Self-training

Deep learning models trained on large number of labeled samples improve the accuracy of many tasks of computer vision. In addition to this, since collecting and labeling vast amount of samples in various domains is difficult, it is important to develop adaptable models to different domains. In unsupervised domain adaptation, given data of labeled samples on source domain, our goal is to learn a cl...