Deep Discriminative Feature Learning for Domain Adaptation

Abstract

Recent advancements have been seen in Deep domain adaptation field, which helps transfer knowledge from a source domain to a related but different target domain, greatly reducing the cost of manual annotation and successfully learning domain invariant features. However, most existing deep domain adaptation methods only align source and target domain distributions, neglecting the class structure information in the source domain, and ultimately leading to domain confusion. To address this issue, we propose a new model for deep domain adaptation, which can simultaneously achieve domain alignment and discriminative feature learning. Specifically, apart from performing domain-invariant embeddings with MMD metric, we utilize a center loss to construct class structure, so as to enhance inter-class separability and intra-class compactness. In addition, our model is effective and easy to implement, compared to other methods. Extensive experiments conducted on two benchmark datasets verify that our model has superior performance over state-of-the-art methods.