Deep Domain Adaptation

Learning with limited labeled data is always a challenge in AI problems, and one of promising ways is transferring well-established source domain knowledge to the target domain, i.e., domain adaptation. Recent researches on transfer learning exploit deep structures for discriminative feature representation to tackle cross-domain disparity. However, few of them are able to joint feature learning and knowledge transfer in a unified deep framework. In this chapter, we develop three novel deep domain adaptation approaches for knowledge transfer. First, we propose a Deep Low-Rank Coding framework (DLRC) for transfer learning. The core idea of DLRC is to jointly learn a deep structure of feature representation and transfer knowledge via an iterative structured low-rank constraint, which aims to deal with the mismatch between source and target domains layer by layer. Second, we propose a novel Deep …