Unsupervised Domain Adaptation VIA Cluster Alignment with Maximum Classifier Discrepancy

One way of addressing the problem of unsupervised domain adaptation (UDA) is to perform adversarial training between two classifiers and their shared feature extractor. The two classifiers are enforced to detect the misaligned regions between the source and target domains, while the feature extractor aligns the features by confusing the classifiers. Although this method yields improvement, it igno...