Abstract
• The paper points out the drawback that existing methods apply entire information of images to adapt. • The paper proposes a new framework to suppress domain-specific variations and extract the domain-shared features. • The paper is the first to explore the ladder network for unsupervised domain adaptation. • The paper skillfully combines supervised learning with unsupervised learning. • Four representative datasets are constructed to verify the performance of our model. Unsupervised Domain Adaptation aims to learn a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain. Most existing approaches learn domain-invariant features by adapting the entire information of each image. However, forcing adaptation of domain-specific components can undermine the effectiveness of learned features. We propose a novel architecture called Deep Ladder Reconstruction-Classification Network (DLaReC) which is designed to learn cross-domain shared contents by suppressing domain-specific variations. The DLaReC adopts an encoder with cross-domain sharing and a target-domain reconstruction decoder. The encoder and decoder are connected with residual shortcuts at each intermediate layer. By this means, the domain-specific components are directly fed to the decoder for reconstruction, relieving the pressure to learn domain-specific variations at later layers of the shared encoder. Therefore, DLaReC allows the encoder to focus on learning cross-domain shared representations and ignore domain-specific variations. DLaReC is implemented by jointly learning three tasks: supervised classification of the source domain, unsupervised reconstruction of the target domain and cross-domain shared representation adaptation. Extensive experiments on Digit, Office31, ImageCLEF-DA and Office-Home datasets demonstrate the DLaReC outperforms state-of-the-art methods on the whole. The average accuracy on the Digit datasets, for instance, is improved from 95.6% to 96.9%. In addition, the result on Amazon → Webcam obtains significant improvement, i.e. , from 91.1% to 94.7%.
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