Results 11 to 20 of about 1,144,226 (279)
Optimal Transport for Domain Adaptation [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data space become more robust when confronted to data depicting the same semantic concepts (the classes), but observed by another observation system with its own ...
Courty, Nicolas +3 more
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FDDS: Feature Disentangling and Domain Shifting for Domain Adaptation
Mathematics, 2023 Domain adaptation is a learning strategy that aims to improve the performance of models in the current field by leveraging similar domain information. In order to analyze the effects of feature disentangling on domain adaptation and evaluate a model’s ...
Huan Chen, Farong Gao, Qizhong Zhang
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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
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Unsupervised Domain Adaptation via Domain-Adaptive Diffusion
, 2023 Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models which have strong capability to gradually convert data distributions across a large gap, we consider to explore the diffusion technique to handle the challenging UDA task ...
Peng, Duo +3 more
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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
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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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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
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Dynamic Instance Domain Adaptation
IEEE Transactions on Image Processing, 2022 Accepted to IEEE T-IP.
Zhongying Deng +5 more
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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
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Unsupervised Domain Adaptation for 3D Point Clouds by Searched Transformations
IEEE Access, 2022 Input-level domain adaptation reduces the burden of a neural encoder without supervision by reducing the domain gap at the input level. Input-level domain adaptation is widely employed in 2D visual domain, e.g., images and videos, but is not utilized for
Dongmin Kang +3 more
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