Results 31 to 40 of about 12,484 (262)
Discriminative and Geometry-Aware Unsupervised Domain Adaptation [PDF]
IEEE Transactions on Cybernetics, 2020 18pages ...
Luo, Lingkun +4 more
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Unsupervised domain adaptation with copula models [PDF]
2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), 2017 IEEE International Workshop On Machine Learning for Signal Processing ...
Tran, Cuong D. +2 more
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Unsupervised domain adaptation with progressive adaptation of subspaces
Pattern Recognition, 2022 Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source domain with domain shift. Most of the existing UDA methods try to mitigate the adverse impact induced by the shift via reducing domain discrepancy.
Li, Weikai, Chen, Songcan
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Simplified Neural Unsupervised Domain Adaptation [PDF]
Proceedings of the 2019 Conference of the North, 2019 Unsupervised domain adaptation (UDA) is the task of modifying a statistical model trained on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain. Existing state-of-the-art UDA approaches use neural networks to learn representations that can predict the ...
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Multibranch Unsupervised Domain Adaptation Network for Cross Multidomain Orchard Area Segmentation
Remote Sensing, 2022 Although unsupervised domain adaptation (UDA) has been extensively studied in remote sensing image segmentation tasks, most UDA models are designed based on single-target domain settings.
Ming Liu +3 more
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Instance Adaptive Self-training for Unsupervised Domain Adaptation [PDF]
, 2020 The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such a problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing scalability and performance.
Mei, Ke +3 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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Model Adaptation: Unsupervised Domain Adaptation Without Source Data
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 In this paper, we investigate a challenging unsupervised domain adaptation setting -- unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be available in some real-world scenarios due to data ...
Li, Rui +4 more
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Privacy-Preserving Unsupervised Domain Adaptation in Federated Setting
IEEE Access, 2020 The training of deep neural networks relies on massive high-quality labeled data which is expensive in practice. To tackle this problem, domain adaptation is proposed to transfer knowledge from label-rich source domain to unlabeled target domain to learn
Lei Song +3 more
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Augmentation based unsupervised domain adaptation [PDF]
, 2022 The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation. However, even the most advanced methods require a huge and diverse amount of data to generalize.
Orbes-Arteaga, Mauricio +7 more
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