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Domain-Augmented Domain Adaptation
, 2022 Unsupervised domain adaptation (UDA) enables knowledge transfer from the labelled source domain to the unlabeled target domain by reducing the cross-domain discrepancy. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies.
Zeng, Qiuhao, Luo, Tianze, Wang, Boyu
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Discriminative Radial Domain Adaptation
IEEE Transactions on Image Processing, 2023 13 pages, 14 ...
Zenan Huang +4 more
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IEEE Access, 2021 Domain adaptation (DA) is a technology that transfers knowledge from the source domain to the target domain. General domain adaptation assume that the source and the target domain have the same label space.
Ningyu He, Jie Zhu
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Self Domain Adapted Network [PDF]
, 2020 Domain shift is a major problem for deploying deep networks in clinical practice. Network performance drops significantly with (target) images obtained differently than its (source) training data. Due to a lack of target label data, most work has focused on unsupervised domain adaptation (UDA).
He, Yufan +4 more
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Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder
Applied Sciences, 2022 Unsupervised domain adaptation involves knowledge transfer from a labeled source to unlabeled target domains to assist target learning tasks. A critical aspect of unsupervised domain adaptation is the learning of more transferable and distinct feature ...
Yi Zhu, Xinke Zhou, Xindong Wu
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Review of Studies on Domain Adaptation [PDF]
Jisuanji gongcheng, 2021 Classical machine learning algorithms assume that the training and testing instances share the same input feature space and data distribution.In many real-world applications, however, this assumption cannot be satisfied, resulting in the failure of the ...
LI Jingjing, MENG Lichao, ZHANG Ke, LU Ke, SHEN Hengtao
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Invertible Autoencoder for Domain Adaptation
Computation, 2019 The unsupervised image-to-image translation aims at finding a mapping between the source ( A ) and target ( B ) image domains, where in many applications aligned image pairs are not available at training.
Yunfei Teng, Anna Choromanska
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Deep adversarial domain adaptation network
International Journal of Advanced Robotic Systems, 2020 The advantage of adversarial domain adaptation is that it uses the idea of adversarial adaptation to confuse the feature distribution of two domains and solve the problem of domain transfer in transfer learning.
Lan Wu +3 more
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C2DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation
Sensors, 2020 Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain.
Han Sun +5 more
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