Representation learning via an integrated autoencoder for unsupervised domain adaptation

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target domain. The key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain discrepancy. Recently, deep learning methods based on autoencoder have achieved sound performance in representation learning, and many dual or serial autoencoder-based methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain adaptation. However, most existing methods of autoencoders just serially connect the features generated by different autoencoders, which pose challenges for the discriminative representation learning and fail to find the real cross-domain features. To address this problem, we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation, called IAUDA. To capture the inter-and inner-domain features of the raw data, two different autoencoders, which are the marginalized autoencoder with maximum mean discrepancy (mAEMMD) and convolutional autoencoder (CAE) respectively, are proposed to learn different feature representations. After higher-level features are obtained by these two different autoencoders, a sparse autoencoder is introduced to compact these inter-and inner-domain representations. In addition, a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local area. MMD Experimental results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.