Cross-Domain Error Minimization for Unsupervised Domain Adaptation [PDF]
, 2021 Accepted by DASFAA ...
Du, Yuntao +4 more
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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
doaj +1 more source
Discriminative and Geometry-Aware Unsupervised Domain Adaptation [PDF]
IEEE Transactions on Cybernetics, 2020 18pages ...
Luo, Lingkun +4 more
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BOOSTED UNSUPERVISED MULTI-SOURCE SELECTION FOR DOMAIN ADAPTATION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 Supervised machine learning needs high quality, densely sampled and labelled training data. Transfer learning (TL) techniques have been devised to reduce this dependency by adapting classifiers trained on different, but related, (source) training data ...
K. Vogt +4 more
doaj +1 more source
Unsupervised Domain Adaptation Based on Pseudo-Label Confidence
IEEE Access, 2021 Unsupervised domain adaptation aims to align the distributions of data in source and target domains, as well as assign the labels to data in the target domain.
Tingting Fu, Ying Li
doaj +1 more source
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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From source to target and back: symmetric bi-directional adaptive GAN [PDF]
, 2017 The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem. It has been shown that source labeled images
Caputo, Barbara +3 more
core +2 more sources
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
openaire +2 more sources
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
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
openaire +2 more sources