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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Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation [PDF]
, 2020 ECCV 2020 ...
Xingchao Peng, Yichen Li, Kate Saenko
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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
doaj +1 more source
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
doaj +1 more source
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.
Ke Mei +3 more
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Generative Adversarial Models for Unsupervised Domain Adaptation in Semantic Segmentation [PDF]
, 2022 In this thesis we implement an unsupervised domain adaptation framework designed for semantic segmentation and tested in a synthetic-to-real adaptation scenario. First, we propose a pixel-level adaptation strategy based on a cross-domain image mapping to
Toldo, Marco
core
Unsupervised Domain Adaptation with Progressive Domain Augmentation
CoRR, 2020 Domain adaptation aims to exploit a label-rich source domain for learning classifiers in a different label-scarce target domain. It is particularly challenging when there are significant divergences between the two domains. In the paper, we propose a novel unsupervised domain adaptation method based on progressive domain augmentation.
Kevin Hua, Yuhong Guo
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Domain‐specific feature recalibration and alignment for multi‐source unsupervised domain adaptation
IET Computer Vision, 2023 Traditional unsupervised domain adaptation (UDA) usually assumes that the source domain has labels and the target domain has no labels. In a real environment, labelled source domain data usually comes from multiple different distributions. To handle this
Mengzhu Wang +6 more
doaj +1 more source
Informative Feature Disentanglement for Unsupervised Domain Adaptation [PDF]
, 2021 Unsupervised Domain Adaptation (UDA) aims at learning a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain with a related but different distribution. The strategy of aligning the two domains in latent feature
Guo, Deke +7 more
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FixBi:bridging domain spaces for unsupervised domain adaptation [PDF]
, 2021 Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from ...
Chang, Hyung Jin; id_orcid +7 more
core +1 more source