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 Transfer Low-rank Coding (DTLC) framework to uncover more shared knowledge across source and target in a multi-layer manner. Specifically, we extend traditional low-rank coding with one dictionary to multi-layer dictionaries by jointly building multiple latent common dictionaries shared by two domains. Third, we propose a novel deep model called “Deep Adaptive Exemplar AutoEncoder”, where we build a spectral bisection tree to generate source-target data compositions as the training pairs fed to autoencoders, and impose a low-rank coding regularizer to ensure the transferability of the learned hidden layer.