Journal
PATTERN RECOGNITION LETTERS
Volume 152, Issue -, Pages 398-405Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2021.10.009
Keywords
Domain adaptation; Deep learning; Deep convolutional neural network; Autoencoder; Transfer learning; Unsupervised learning
Categories
Funding
- National Natural Science Foundation of China [61701508]
- Hunan Provincial Natural Science Foundation of China [2018JJ3613]
- Sci-ence and Technology on Electro-optic Control Laboratory [20165188004]
- China Scholarship Council
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DLaReC is an architecture designed for unsupervised domain adaptation that learns cross-domain shared contents by suppressing domain-specific variations, outperforming state-of-the-art methods on several datasets.
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%. (c) 2021 Published by Elsevier B.V.
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