Deep ladder reconstruction-classification network for unsupervised domain adaptation
Deng, Wanxia; Su, Zhuo; Qiu, Qiang; Zhao, Lingjun; Kuang, Gangyao; Pietikäinen, Matti; Xiao, Huaxin; Liu, Li (2021-10-12)
Deng, W., Su, Z., Qiu, Q., Zhao, L., Kuang, G., Pietikäinen, M., Xiao, H., & Liu, L. (2021). Deep ladder reconstruction-classification network for unsupervised domain adaptation. Pattern Recognition Letters, 152, 398–405. https://doi.org/10.1016/j.patrec.2021.10.009
© 2021 Published by Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe2022022520822
Tiivistelmä
Abstract
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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