Fully semisupervised framework for visual domain adaptation

Unsupervised domain adaptation aims to utilize knowledge from a source domain to improve learning in a target domain in cases in which abundant labeled samples are available in the source domain, but no labels exist in the target domain. The two domains have the same feature space and label space but different distributions. Our study proposes a semisupervised framework in which labeled samples in the source domain and unlabeled target samples are fully utilized to learn a better classifier. The framework contains two stages: semisupervised feature learning and semisupervised classifier learning. In the first stage, a transformation is learned using labeled source samples and unlabeled target samples to map these data into a representation. In the second stage, a classifier is learned using all samples in the source and target domains of the representation. Furthermore, we propose a semisupervised feature learning approach (i.e., cross-domain discriminative analysis) to learn the transformation in the first stage by reducing the distribution discrepancies between domains and preserving discriminative information in the original data. In our experiments, image classification tasks were conducted using several well-known cross-domain datasets. The proposed method outperformed the state-of-the-art methods in most cases.