Results 31 to 40 of about 4,969,023 (298)
Benchmarking Domain Adaptation Methods on Aerial Datasets
Sensors, 2021 Deep learning grew in importance in recent years due to its versatility and excellent performance on supervised classification tasks. A core assumption for such supervised approaches is that the training and testing data are drawn from the same ...
Navya Nagananda +6 more
doaj +1 more source
Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by labeled samples from the source ...
Li, Jichang +3 more
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Unsupervised Multi-source Domain Adaptation Without Access to Source Data [PDF]
Computer Vision and Pattern Recognition, 2021 Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled domain by transferring knowledge from a separate labeled source domain.
Sk. Miraj Ahmed +4 more
semanticscholar +1 more source
IEEE Access, 2023 In traditional machine learning, the training and testing data are assumed to come from the same independent and identical distributions. This assumption, however, does not hold up in real-world applications, as differences between the training and ...
Obsa Gilo +3 more
doaj +1 more source
Dynamic Instance Domain Adaptation
IEEE Transactions on Image Processing, 2022 Accepted to IEEE T-IP.
Zhongying Deng +5 more
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Domain Adaptation for Time Series Under Feature and Label Shifts [PDF]
International Conference on Machine Learning, 2023 Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents challenges due to the dynamic temporal structure variations across ...
Huan He +5 more
semanticscholar +1 more source
Sensors, 2022 Unsupervised domain adaptation, which aims to alleviate the domain shift between source domain and target domain, has attracted extensive research interest; however, this is unlikely in practical application scenarios, which may be due to privacy issues ...
Xuejun Zhao +6 more
doaj +1 more source
Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation [PDF]
Computer Vision and Pattern Recognition, 2023 Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is ...
Mattia Litrico +2 more
semanticscholar +1 more source
Spectral Normalization for Domain Adaptation
Information, 2020 The transfer learning method is used to extend our existing model to more difficult scenarios, thereby accelerating the training process and improving learning performance.
Liquan Zhao, Yan Liu
doaj +1 more source
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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