Domain adaptation and generalization for visual recognition - Niu, Li (Nanyang Technological University, 2017)
In many visual recognition tasks, the domain distribution mismatch between the training set (i.e., source domain) and the test set (i.e., target domain) may cause the performance of the classifier learnt from the training set to be significantly degraded on the test set. The solutions to address the domain distribution mismatch can be classified into Domain Adaptation (DA) and Domain Generalization (DG). Specifically, DA utilizes the unlabeled target domain data in the training phase to reduce the domain distribution mismatch while DG aims to learn the classifier on the source domain which can generalize well to any unseen target domain. This thesis focuses on DA and DG for visual recognition.