Results 21 to 30 of about 389,599 (264)
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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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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2019 International Joint Conference on Neural Networks (IJCNN), 2019 Domain adaptation investigates the problem of leveraging knowledge from a well-labeled source domain to an unlabeled target domain, where the two domains are drawn from different data distributions. Because of the distribution shifts, different target samples have distinct degrees of difficulty in adaptation.
Jingjing Li 0001 +4 more
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CoRR, 2020 In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different kinds of priors or directly minimize the domain discrepancy to address this problem, which lack flexibility in handling real-world situations.
Shuhao Cui +4 more
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2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Vanilla unsupervised domain adaptation methods tend to optimize the model with fixed neural architecture, which is not very practical in real-world scenarios since the target data is usually processed by different resource-limited devices. It is therefore of great necessity to facilitate architecture adaptation across various devices. In this paper, we
Rang Meng +9 more
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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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CoRR, 2021 Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source. To achieve this, DA methods include a source classification objective to extract the source knowledge and a domain alignment objective to diminish the domain shift, ensuring knowledge ...
Fangrui Lv +7 more
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Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization
Complexity, 2018 Domain adaptation has recently attracted attention for visual recognition. It assumes that source and target domain data are drawn from the same feature space but different margin distributions and its motivation is to utilize the source domain instances
Yiwei He +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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